text:
Abbott, Weir 2-3 at NHK Trophy
While World Champion Brian Joubert and three-time U.S. Champion Johnny Weir resurrected their seasons Friday at the NHK Trophy in Nagano, Japan, reigning Grand Prix Final Champion Jeremy Abbott started his off on a high note.
Joubert, who finished a disapointing fourth at Trophee Eric Bompard, leads the short program with 85.35 points. Abbott is second with 83.00 and Weir, who missed the podium in his season debut at Rostelecom Cup, is third with 78.35. Two-time World Junior Champion Adam Rippon (67.15), who won bronze at Trophee Eric Bompard, is eighth.
"I was very nervous coming in, because it's a lot of new firsts for me," Abbott said. "This is the first full competition I've done [this season] and I have a new coach and a lot of new things happen to my life. So I was very anxious to see how it would turn out. My training has been going very well and I felt that I just kept everything the same [like in practice]."
Abbott opened his elegant performance to "A Day in the Life" by The Beatles with a solid triple flip-triple toeloop combination followed by a triple Axel and triple Lutz. He was awarded a Level 4 for the change foot sit spin and the flying sit spinwhile his combination spin and both step sequences were graded Level 3.
Skating to a rumba program he titled "I Love You, I Hate You", Weir landed a triple Lutz-triple toe, a triple Axel and triple flip. He picked up a Level 4 for two spins, a Level 3 for the combination spin and another Level 3 the circular footwork to post a seasons best score.
"After my season premiere in Russia, just two weeks ago I was very angry with myself and the way I've skated there, Weir said. "So when I went home in the short period between the two competitions I worked very hard to change my mentality. When I went on the airplane I got very sick and I'm proud that I could still push through this situation and have a fever and still compete and really show the level of training that I've done this summer."
Joubert nailed a quadruple-triple toe combination and a triple Axel in his fast-paced routine to "Rise" by Safri Duo, but he did a three-turn out of his triple Lutz. "I am very happy with today's performance. It was difficult for me after my first Grand Prix. I was very disappointed there, and it was important for me to do this kind of program today. This gives me confidence for the rest of the competition," he said.
Rippon fell during his performance.
NHK Trophy is the fourth event of the 2009 International Skating Union (ISU) Grand Prix of Figure Skating circuit. The series, now in its 15th season, consists of six international events in a cumulative, point-scoring format. The top six scoring athletes in each discipline move onto the Grand Prix Final, Dec. 3-6, in Tokyo, Japan, which will be combined with the ISU Junior Grand Prix of Figure Skating Final for the second consecutive season.
The six Grand Prix events are Trophee Eric Bompard, Oct. 15-18, in Paris, France; Rostelecom Cup, Oct. 22-25, in Moscow Russia; Cup of China, Oct. 29-Nov. 1, in Beijing, China; NHK Trophy, Nov. 5-8, in Nagano, Japan; Skate America, Nov. 12-15, in Lake Placid, N.Y.; and Skate Canada, Nov. 19-22, in Kitchener, Canada.
The points toward the Grand Prix Final that are awarded for each place are 15 points for first; 13 points for second; 11 points for third; nine points for fourth; seven points for fifth; five points for sixth place; four points for seventh place; and three points for eighth. In pair skating, only the top six receive points.
Related tags : Adam RipponJeremy AbbottJohnny Weir
In debut at NHK, Denney and Barrett in third
Ashley Wagner wins short at NHK Trophy
ISU announces 2019-2020 Grand Prix Series assignments
Preview: Skate America ladies event
Rippon's road to Tokyo goes through NHK


Hypervisual has powerful tools to embed your products with variant selectors and an add to cart button. But sometimes you want to create a link which adds a product (or products!) to your cart without the product image, product name or variant selector. Here's how to do it.
When you visit that page you'll get a list of all of your product variants, the add to cart URLs and the direct to checkout URLs.
and replace 12345 with your variant ID. To find the variant ID, the easiest way is to open the product inside your Shopify admin, click 'Edit' next to the particular variant you want to have added to the basket, then pick the variant ID up from the URL that's then inside your browser address bar: it's the very last number at the end of the web address.
To learn how to make direct-to-checkout links without Hypervisual installed, read this Shopify help article: Use permalinks to pre-load the cart.


So many people across Ecumen have made Ecumen Awakenings possible and are contributing to the program's learnings and growth. It is innovation that empowers and honors those with Alzheimer's and other types of dementia while underscoring our mission and vision. Saturday's front-page article in the Minneapolis Star Tribune features a long story on this great work.
By the roadside on Highway 8 coming into Chisago City a green giant of a chair entices passers-by to come over, sit down and be swallowed up.
Ecumen Parmly LifePointes in Chisago City, MN, has been awarded a contract with the U.S. Department of Veterans Affairs to provide long and short-term skilled nursing home care for veterans.


text:
Tallahassee Criminal Attorney > Tallahassee Illegal Sentencing Attorney
Tallahassee Illegal Sentencing Attorney
Every criminal offense in Florida carries certain minimum and maximum statutory penalties. The sentencing judge has broad discretion to determine a sentence within those parameters, taking into account a variety of factors, such as a defendant's prior criminal history, the nature of the present crime, and even whether or not the defendant is employed. In felony cases, prosecutors are also required to prepare a sentencing "score sheet" that assigns numerical values to a wide range of factors. This final result of this score sheet can mean the difference between prison and probation.
But as with any human activity, sentencing is not perfect. Indeed, there are often cases where a judge imposes a sentence that is simply not permitted under Florida law. Fortunately, the law provides a remedy for defendants to challenge such illegal sentences. And the Tallahassee illegal sentencing attorneys at Luke Newman, P.A., are here to help.
What Makes a Sentence Illegal?
Under the rules governing Florida criminal procedure, a court has the authority to "at any time correct an illegal sentence." This includes while an appeal is pending before a higher court. Some of the more common grounds for correcting an illegal sentence include:
The trial court imposed a prison term that was greater than the statutory maximum specified for a particular criminal offense.
The trial court failed to credit the defendant with any time served in jail while awaiting trial.
The prosecution submitted an incorrect score sheet that the trial court ultimately relied upon in imposing a sentence.
The trial court incorrectly applied certain "enhancements" that resulted in a harsher sentence than is permitted by law. For example, some crimes are subject to a higher mandatory minimum sentence if they involve the use of a firearm.
It is important to understand that when a trial or appellate court sets aside an illegal sentence, that does not mean the defendant is free to go. An illegal sentence does not affect the legality of the underlying conviction. But the defendant is entitled to a new sentencing hearing that follows the proper laws and guidelines.
Contact Tallahassee Criminal Defense Attorney Luke Newman Today
While the rules permit a sentencing judge to correct an illegal sentence on their own initiative, it is often left to the defendant to bring the problem to the court's attention. Ideally, a well-represented defendant will bring any potential issues to the judge before an illegal sentence is imposed. However, if through error or mistake the court still manages to exceed its constitutional or legal authority, it is imperative for the defendant to take immediate action rather than wait for the court to act.
At Luke Newman, P.A., we represent criminal defendants throughout the Tallahassee region whose criminal sentences contain clear mistakes or errors of law. Nobody should be forced to serve an illegal or improper sentence. We can help make sure that does not happen in your case. So if you are involved in a criminal matter, please contact us today to schedule a consultation.

redpajama_set_name: RedPajamaCommonCrawl


Match ends, Clyde 5, Berwick Rangers 0.
Second Half ends, Clyde 5, Berwick Rangers 0.
Attempt missed. David Goodwillie (Clyde) right footed shot from the right side of the six yard box is close, but misses to the right.
Goal! Clyde 5, Berwick Rangers 0. Chris McStay (Clyde) right footed shot from the centre of the box to the bottom left corner. Assisted by David Goodwillie.
Attempt saved. Ross Lyon (Clyde) right footed shot from the left side of the box is saved in the centre of the goal.
Corner, Clyde. Conceded by Ryan Goodfellow.
Attempt saved. Ray Grant (Clyde) left footed shot from outside the box is saved in the bottom left corner.
Attempt missed. Chris McStay (Clyde) header from the centre of the box is close, but misses to the right following a set piece situation.
Tom Lang (Clyde) wins a free kick on the left wing.
Ally Love (Clyde) wins a free kick in the attacking half.
Aidan McIlduff (Berwick Rangers) wins a free kick on the right wing.
Attempt missed. Chris McStay (Clyde) right footed shot from outside the box misses to the right.
Foul by Kevin Nicoll (Clyde).
Attempt missed. David Goodwillie (Clyde) right footed shot from outside the box is high and wide to the right.
Goal! Clyde 4, Berwick Rangers 0. David Goodwillie (Clyde) right footed shot from outside the box to the top right corner. Assisted by Ally Love.
Substitution, Clyde. Ross Lyon replaces Mark Lamont.
Substitution, Berwick Rangers. Jack Ogilvie replaces Sean Murphy.
Attempt missed. Martin McNiff (Clyde) header from the centre of the box is close, but misses the top left corner following a corner.
Corner, Clyde. Conceded by Andrew Forbes.
Attempt saved. Ouzy See (Berwick Rangers) right footed shot from outside the box is saved in the centre of the goal.
Attempt blocked. Cameron Blues (Berwick Rangers) left footed shot from very close range is blocked.
Attempt saved. Chris McStay (Clyde) header from the centre of the box is saved in the top centre of the goal.
Substitution, Berwick Rangers. Ouzy See replaces Grant Rose.
Substitution, Clyde. Kevin Nicoll replaces John Rankin.
Delay in match (Berwick Rangers).
Attempt missed. Cameron Blues (Berwick Rangers) header from the centre of the box misses to the left.
Goal! Clyde 3, Berwick Rangers 0. John Rankin (Clyde) from a free kick with a left footed shot to the bottom right corner.
David Goodwillie (Clyde) wins a free kick in the attacking half.
Substitution, Clyde. Ally Love replaces Kristoffer Syvertsen.
Attempt missed. John Rankin (Clyde) right footed shot from outside the box misses to the left.
Goal! Clyde 2, Berwick Rangers 0. Scott Rumsby (Clyde) header from the centre of the box to the bottom right corner. Assisted by John Rankin following a corner.
Corner, Clyde. Conceded by Jack Cook.
Attempt missed. Cameron Blues (Berwick Rangers) right footed shot from the centre of the box misses to the left.
Attempt saved. Kristoffer Syvertsen (Clyde) right footed shot from the centre of the box is saved in the bottom left corner.
Second Half begins Clyde 1, Berwick Rangers 0.
First Half ends, Clyde 1, Berwick Rangers 0.
Attempt missed. Chris McStay (Clyde) right footed shot from outside the box is high and wide to the left.
Chris McStay (Clyde) wins a free kick in the attacking half.
Corner, Berwick Rangers. Conceded by Chris McStay.
Tom Lang (Clyde) wins a free kick on the right wing.
Attempt blocked. David Goodwillie (Clyde) left footed shot from the centre of the box is blocked.
Jordan Orru (Berwick Rangers) is shown the yellow card for a bad foul.
Kristoffer Syvertsen (Clyde) wins a free kick in the attacking half.
Ray Grant (Clyde) wins a free kick on the right wing.
Attempt missed. Barry Cuddihy (Clyde) right footed shot from outside the box is high and wide to the left.
Foul by Kristoffer Syvertsen (Clyde).
Attempt missed. Martin McNiff (Clyde) header from the right side of the six yard box is too high following a corner.
Jack Cook (Berwick Rangers) wins a free kick in the attacking half.
Martin McNiff (Clyde) wins a free kick in the defensive half.
Goal! Clyde 1, Berwick Rangers 0. Barry Cuddihy (Clyde) right footed shot from the right side of the box to the top right corner. Assisted by John Rankin.


text:
« When Is It Too Soon to Call a Catastrophe a "Genocide"? | Main | No standing when abortion law privately enforced »
More stupid lawsuits, ep. 81
Something called the "Job Creators Network" has sued MLB, MLB Commissioner Rob Manfred, the MLBPA, MLBPA Executive Director Tony Clar, and a bunch of John Does, claiming constitutional (equal protection and Dormant Commerce clause) and contract claims over MLB moving the All-Star Game from Atlanta to protest Georgia's voter-suppression laws. Nothing new here; more bad lawyering by bad lawyers using the courts to make political noise. Some thoughts on where this goes wrong.
MLB Under Color:
The § 1983 claim depends on MLB acting under color. The complaint tries to get there two ways, neither of which works.
The first, passing argument, is that MLB is not covered by the antitrust laws, a substantial benefit. Receipt of governmental benefits, disconnected from the challenged conduct, is not sufficient to place a private actor under color. This is not a case in which government gave a private actor a benefit on condition of the private actor doing something constitutionally violative. Quite the opposite. MLB's decision to move the game pissed off some congressional demagogues, who threatened to strip MLB of its antitrust exemption.
Moreover, if the antitrust exemption were sufficient, the alleged benefit comes from the federal government, not the state or local government. MLB therefore would be color of federal law and this would be a Bivens claim (in a new context, so not going anywhere), not a § 1983 claim.
The primary argument is that MLB teams act under color by virtue of playing in publicly owned or publicly financed stadiums; thus MLB, as an association of those teams, acts under color. Two problems. First, while I agree that playing in public stadiums places teams under color for some purposes--namely running those ballparks during games--it does not make them under color for all purposes. The Yankees are perhaps bound by the First Amendment in regulating fans' cheering speech during games at the publicly owned stadium; they are not bound by equal protection in firing a ticket-office employee. So if the teams are not under color for all purposes, MLB is not under cover for all purposes. Second, and more conclusive, a private association of state actors located in multiple states does not act under color because it is not tied to the law of any state. The NCAA does not act under color despite having actual state entities--public universities--from multiple states as members. It follows that MLB, which stands in the same position to teams as the NCAA does to schools, does not act under color.
Diversity Jurisdiction:
This is minor and not outcome-determinative, but the Civ Pro geek in me remains amazed at how often lawyers get diversity wrong.
According to the complaint, the following is true: The JCN is a not-for-profit corporation, incorporated in DC with its PPB in Texas. MLB is an unincorporated association whose members are the 30 teams; it is a New York entity with its PPB in New York. The MLBPA is the players' union, a New York entity with its PPB in New York. Manfred is a New York citizen and Clark a New Jersey citizen. The complaint does not say so, but I believe the plaintiffs see this as JCN(TX/DC) v. MLB (NY), MLBPA (NY), Manfred (NY), and Clark (NJ).
But an unincorporated association's state of creation or PPB is irrelevant; what matters is the citizenship of its members. The complaint acknowledges that MLB's members are the 30 teams, all of which are corporations or unincorporated association; if the latter, we need further level(s) of inquiry as to the members/partners of each team and perhaps the members/partners of each member. MLB therefore is not a New York citizen (or not solely a New York citizen); it is a citizen of any state in which a member/partner in any team ownership group is a citizen. We do not know every state, I imagine at least one team has at least one member who is a citizen of Texas or DC. Similarly, a union's state of creation or PPB is irrelevant; it is a citizen of every state in which a union member is a citizen. Again, I imagine at least one current MLB player is a citizen of DC or Texas.
This does not matter to the outcome of the case, because the complaint alleges (and there is) supplemental jurisdiction over the state claims. But I become skeptical of any complaint when the attorney gets the basics so wrong.
JCN purports to be an association of small businesses injured by MLB moving the game. It asserts associational standing on behalf of its members and organizational standing for the time and money it has spent fighting MLB's actions.
The problem is that the claim seeks primarily damages as a remedy, whereas associational standing works in injunctive actions. One element of the Hunt test for associational standing is that "neither the claim asserted nor the relief requested requires the participation of individual members in the lawsuit." Damage are, by definition, individualized to each plaintiff and thus require the participation of each member. The complaint attempts to get around that by asking the court to order that the defendants pay damages into a common fund, from which moneys are distributed to each plaintiff. But that is not how damages are calculated or awarded. I cannot think of a major constitutional case in which an association has been able to pursue damages on behalf of its members.
The complaint asks for compensatory damages of at least $ 10 million and punitive damages of at least $ 1 billion, as well as an injunction ordering MLB to move the game back to Atlanta. That injunction is not happening. The punitive damages request is interesting because a punitive-damages ration exceeding 10:1 presumptively violates due process.
The least-stupid contract claim might be promissory estoppel, although that still fails. But even if it worked, the damages on a P/E claim are limited to what was spent in reliance on the promises, not what they would have made had the defendants followed through on their promise. Did businesses spend $ 10 million+ on the expectation of the game coming to Atlanta?
Whither the First Amendment
At its core, the claim here is that by engaging in the First Amendment activity of protesting Georgia election policy through its business decisions, MLB, et al. interfered with the power of Georgia to enact policy and the equal protection rights of those who support those policies. The Complaint spends some time defending Georgia's new laws, as if the propriety (in the plaintiff's views) of the laws lessens the First Amendment rights of those who protest. I do not believe the level of First Amendment protection for expressive activities turns on the "correctness" of the position asserted. That would have some broad implications.
Posted by Howard Wasserman on June 2, 2021 at 08:58 AM in Civil Procedure, Constitutional thoughts, Howard Wasserman, Judicial Process, Law and Politics | Permalink


EVEN Yasser Arafat's mother-in-law has not escaped the squeeze.
Raymonda Tawil, co-owner of the Palestine Press Service, has had to lay off all the agency's journalists around the occupied territories, and slash the Jerusalem staff's salary by 30 percent. And the fact that her daughter is married to the chairman makes no difference when the Palestine Liberation Organization (PLO) is cutting costs.
The PLO's financial crisis, brought on by Arab governments that have canceled their contributions since the Palestinians sympathized with Iraq's Saddam Hussein in the Gulf war, has hit many Palestinian institutions in the occupied territories that once depended on semiclandestine subsidies from PLO headquarters in Tunis.
Hospitals, schools, and universities are all feeling the pinch. But nowhere are the effects as clear as in the Palestinian press.
Two weeks ago, the staff of the daily Al Fajr received their notice and a letter from the publisher warning that the paper would close July 24th. The blow came three months after another of the occupied territories' four dailies, Al Shaab, folded when the staff walked out to protest their not being paid for several months.
Two weekly magazines have also gone under recently, leaving the field to Al Quds, which most Palestinians read for its small ads and social register, and An Nahar, whose subsidy from the Jordanian government is an open secret.
Palestinian journalists and editors do not blame their travails only on cuts in foreign funding, however. The local economy, always weak because of the Israeli occupation, has dropped off 30 percent since the Israeli government closed off the territories three months ago.
"In a weak economy with limited advertising, it is almost impossible to survive on income from the local market," complains Hana Siniora, the editor of Al Fajr.
It is not only potential advertisers who have been hurt. "If you give someone the option of buying either a newspaper or four pieces of pita bread for a shekel, he'll opt for the second choice," says Zakaria al-Qaq, a Palestinian political analyst.
But many Palestinians point to the newspapers' content as sufficient explanation of their poor sales.
"When the PLO decided to open its own newspapers in the early 1970s, it just wanted them to praise the PLO," says Ruba al-Husari, correspondent in the occupied territories for the most respected Arab newspaper, the London-based Al Hayat. "They didn't care about having good quality, credible newspapers with professional journalists."
The standards of the journalists who work on the local papers are often woefully inadequate, press critics say, as many are chosen more for political than professional reasons. The journalists' union, the Arab Journalists' Association, is more a battleground for various factions of the PLO than a body promoting professional standards.
At the same time, editors complain, their work is made doubly hard by strict Israeli censorship. "A merchant in East Jerusalem will see a clash [between Palestinians and Israeli soldiers] in the street one day," says Mr. Siniora, "and the next day he will see nothing in the paper about it. And he blames the journalists."
Siniora says that with staff cuts and other budgetary measures, he can make up the $20,000-a-month deficit that the PLO no longer covers, and that he will be able to keep the paper alive.
But it will be a struggle. And in the current economic and political circumstances, the incentives are meager for any investor thinking of launching a new Palestinian paper.
Mr. Qaq, for one, is biding his time, even though he has been granted a publisher's license by the Israelis. Short of funds and professional staff, he awaits a clearer Palestinian future before launching himself into the business. If that day comes, he says, "we will need a complete rehabilitation of the Palestinian press."


text:
College Board Partners with Public Impact and N.C. School of Science and Mathematics to Reach Rural Students in North Carolina
The College Board and the North Carolina School of Science and Mathematics
Rural Students AP
Chicago Public Schools Expands Access to AP Programs at 6 More High Schools
Camille Boxhill, Associate Director, Professional Communications, The College Board
Alexis Hernandez, senior at Thomas Kelly High School, reflected on his Advanced Placement (AP) journey and recalled how he was the only student allowed to enroll in AP courses as a sophomore. That's no longer the case.
AP AP Capstone
David Coleman Appoints Jeremy Singer as President of the College Board
Abby Jacobs, Senior Director of Communications, the College Board
On January 9, 2019, David Coleman appointed Jeremy Singer as President of the College Board. He will continue to report into David Coleman, who will remain CEO of the College Board.
David Coleman Jeremy Singer College Board
Clarifying the College Board's Score Review Process
Student scores are never held or cancelled simply because of score gains. Score reviews are triggered by a range of factors that cast doubt on the validity of a score.


text:
\section{Introduction}
\label{sec:Introduction}
6D object pose estimation is a fundamental task of 3D semantic analysis with many real-world applications, such as robotic grasping \cite{moped, wu2020grasp}, augmented reality \cite{handsAR}, and autonomous driving \cite{kitti,levinson2011towards,wang2019frustum,deng2022vista}. Non-linearity of the rotation space of $SO(3)$ makes it hard to handle this nontrivial task through direct pose regression from object observations \cite{discriminative, gradient, hausdorff,posecnn,densefusion,PRGCN,FSNet,dualPoseNet,SS-Conv,lin2022category}. Many of the data-driven methods \cite{hourglass, localaffine, orb,pvnet,learning6d, localrgbd, independent,pvn3d,NOCS, SPD} thus achieve the estimation by learning point correspondence between camera and object coordinate systems.

\begin{figure}[t]
  \centering
  \includegraphics[width=1.0\linewidth]{FigIntro_flatten.pdf}
    \caption{Illustrations of two kinds of point correspondence between camera coordinate system (cam) and object coordinate system (obj). Best view in the
    electronic version.}
    \label{fig:intro}
\end{figure}

Given a partial object observation in camera coordinate system along with its CAD model in object coordinate one, we show in Fig. \ref{fig:intro} two possible ways to build point correspondence: i) inferring the observed points in object coordinate system for partial-to-partial correspondence; ii) inferring the sampled points of CAD model in camera coordinate system for complete-to-complete correspondence. These two kinds of correspondence show different advantages. The partial-to-partial correspondence is of higher qualities than the complete-to-complete one due to the difficulty in shape completion, while the latter is more robust to figure out poses for objects with severe occlusions, which the former can hardly handle with.

While these methods are promising by solving 6D poses from point correspondence (\eg, via a PnP algorithm), their surrogate correspondence objectives are a step away from the true ones of estimating 6D object poses, thus making their learnings suboptimal for the end task \cite{wang2021gdr}.
To this end, we present a novel method to realize the above two ways of correspondence establishment in the feature space via dual newly proposed \textit{Feature Disengagement and Alignment (FDA) modules}, and directly estimate object poses from feature pairs of two coordinate systems, which are weighted by confidence scores measuring the qualities of deep correspondence. We term our method as \textit{Deep Correspondence Learning Network}, shortened as \textit{DCL-Net}. Fig. \ref{fig:overview} gives the illustration.

For the partial object observation and its CAD model, DCL-Net firstly extracts their point-wise feature maps in parallel; then dual Feature Disengagement and Alignment (FDA) modules are designed to establish, in feature space, the partial-to-partial correspondence and the complete-to-complete one between camera and object coordinate systems. Specifically, each FDA module takes as inputs two point-wise feature maps, and disengages each feature map into individual pose and match ones; the match feature maps of two systems are then used to learn an attention map for building deep correspondence; finally, both pose and match feature maps are aligned and paired across systems based on the attention map, resulting in pose and match feature pairs, respectively. DCL-Net aggregates two sets of correspondence together, since they bring complementary advantages, by fusing the respective pose and match feature pairs of two FDA modules. The aggregated match feature pairs are used to learn confidence scores for measuring the qualities of deep correspondence, while the pose ones are weighted by the scores to directly regress object poses. A confidence-based pose refinement network is also proposed to further improve the results of DCL-Net in an iterative manner. Extensive experiments show that DCL-Net outperforms existing methods for 6D object pose estimation on three well-acknowledged datasets, including YCB-Video \cite{YCB}, LineMOD \cite{LineMod}, and Occlusion-LineMOD\cite{learning6d}; remarkably, on the more challenging Occlusion-LineMOD, our DCL-Net outperforms the state-of-the-art method \cite{FFB6D} with an improvement of $4.4\%$ on the metric of ADD(S), revealing the strength of DCL-Net on handling with occlusion. Ablation studies also confirm the efficacy of individual components of DCL-Net. Our technical contributions are summarized as follows:
\begin{itemize}
  \item We design a novel \textit{Feature Disengagement and Alignment (FDA) module} to establish deep correspondence between two point-wise feature maps from different coordinate systems; more specifically, FDA module disengages each feature map into individual pose and match ones, which are then aligned across systems to generate pose and match feature pairs, respectively, such that deep correspondence is established within the aligned feature pairs.
  \item We propose a new method of \textit{Deep Correspondence Learning Network} for direct regression of 6D object poses, termed as DCL-Net, which employs dual FDA modules to establish, in feature space, partial-to-partial correspondence and complete-to-complete one between camera and object coordinate systems, respectively; these two FDA modules bring complementary advantages.
  \item Match feature pairs of dual FDA modules are aggregated and used for learning of confidence scores to measure the qualities of correspondence, while pose feature pairs are weighted by the scores for estimation of 6D pose; a confidence-based pose refinement network is also proposed to iteratively improve pose precision.
\end{itemize}

\section{Related Work}
\label{sec:RelatedWork}

\subsubsection{6D Pose Estimation from RGB Data} This body of works can be broadly categorized into three types: i) holistic methods \cite{discriminative, gradient, hausdorff} for directly estimating object poses; ii) keypoint-based methods \cite{hourglass, localaffine, orb}, which establish 2D-3D correspondence via 2D keypoint detection, followed by a PnP/RANSAC algorithm to solve the poses; iii) dense correspondence methods \cite{learning6d, localrgbd, independent, pvnet}, which make dense pixel-wise predictions and vote for the final results.

Due to loss of geometry information, these methods are sensitive to lighting conditions and appearance textures, and thus inferior to the RGB-D methods.

\subsubsection{6D Pose Estimation from RGB-D Data} Depth maps provide rich geometry information complementary to appearance one from RGB images. Traditional methods \cite{learning6d, LineMod, rios2013discriminatively, tejani2014latent,wohlhart2015learning } solve object poses by extracting features from RGB-D data and performing correspondence grouping and hypothesis verification. Earlier deep methods, such as PoseCNN \cite{posecnn} and SSD-6D \cite{ssd6d}, learn coarse poses firstly from RGB images, and refine the poses on point clouds by using ICP \cite{ICP} or MCN \cite{MCN}. Recently, learning deep features of point clouds becomes an efficient way to improve pose precision, especially for methods \cite{densefusion, PRGCN} of direct regression, which make efforts to enhance pose embeddings from deep geometry features, due to the difficulty in the learning of rotations from a nonlinear space. Wang \textit{et al.} present DenseFusion \cite{densefusion}, which fuses local features of RGB images and point clouds in a point-wise manner, and thus explicitly reasons about appearance and geometry information to make the learning more discriminative; due to the incomplete and noisy shape information, Zhou \textit{et al.} propose PR-GCN \cite{PRGCN} to polish point clouds and enhance pose embeddings via Graph Convolutional Network. On the other hand, dense correspondence methods show the advantages of deep networks on building the point correspondence in Euclidean space; for example, He \textit{et al.} propose PVN3D \cite{pvn3d} to regress dense keypoints, and achieve remarkable results. While promising, these methods are usually trained with surrogate objectives instead of the true ones of estimating 6D poses, making the learning suboptimal for the end task.

Our proposed DCL-Net borrows the idea from dense correspondence methods by learning deep correspondence in feature space, and weights the feature correspondence based on confidence scores for direct estimation of object poses. Besides, the learned correspondence is also utilized by an iterative pose refinement network for precision improvement.

\section{Deep Correspondence Learning Network}
\label{sec:method}

\begin{figure}[t]
  \centering
  \includegraphics[width=1.0\linewidth]{FigOverview.pdf}
    \caption{An illustration of DCL-Net. Given object observation and its CAD model, DCL-Net first extracts their point-wise features $\bm{F}^{\mathcal{X}_c}$ and $\bm{F}^{\mathcal{Y}_o}$, separately; then dual Feature Disengagement and Alignment (FDA) modules are employed to establish, in feature space, partial-to-partial correspondence and complete-to-complete one between camera and object coordinate systems, respectively, which result in aggregated pose and match feature pairs; the match feature pairs are used to learn confidence scores $\bm{s}$ for measuring the qualities of deep correspondence, while the pose ones are weighted by $\bm{s}$ for estimating 6D object pose $(\bm{R}, \bm{t})$. Best view in the electronic version.}
    \label{fig:overview}
\end{figure}

Given the partial object observation $\mathcal{X}_{c}$ in the camera coordinate system, along with the object CAD model $\mathcal{Y}_{o}$ in the object coordinate one, our goal is to estimate the 6D pose $(\bm{R}, \bm{t})$ between these two systems, where $\bm{R} \in SO(3)$ stands for a rotation, and $\bm{t} \in \mathbb{R}^3$ for a translation.

Fig. \ref{fig:overview} gives the illustration of our proposed \textit{Deep Correspondence Learning Network} (dubbed \textit{DCL-Net}). DCL-Net firstly extracts point-wise features of $\mathcal{X}_{c}$ and $\mathcal{Y}_{o}$ (cf. Sec. \ref{subsec:pointwise_feature_extraction}), then establishes correspondence in feature space via \textit{dual Feature Disengagement and Alignment modules} (cf. Sec. \ref{subsec:dual_feature_disengagement_and_alignment}), and finally regresses the object pose $(\bm{R}, \bm{t})$ with confidence scores based on the learned deep correspondence (cf. Sec. \ref{subsec:PoseEstimation}). The training objectives of DCL-Net are given in Sec. \ref{subsec:training_objectives}. A confidence-based pose refinement network is also introduced to iteratively improve pose precision (cf. Sec. \ref{subsec:confidence_based_pose_refinement}).

\subsection{Point-wise Feature Extraction}
\label{subsec:pointwise_feature_extraction}
We represent the inputs of the object observation $\mathcal{X}_{c}$ and its CAD model $\mathcal{Y}_{o}$ as $(\bm{I}^{\mathcal{X}_{c}}, \bm{P}^{\mathcal{X}_{c}})$ and $(\bm{I}^{\mathcal{Y}_{o}}, \bm{P}^{\mathcal{Y}_{o}})$ with $N_{\mathcal{X}}$ and $N_{\mathcal{Y}}$ sampled points, respectively, where $\bm{P}$ denotes a point set, and $\bm{I}$ denotes RGB values corresponding to points in $\bm{P}$.
As shown in Fig. \ref{fig:overview}, we use two parallel backbones to extract their point-wise features $\bm{F}^{\mathcal{X}_{c}}$ and $\bm{F}^{\mathcal{Y}_{o}}$, respectively.
Following \cite{SA-SSD}, both backbones are built based on 3D Sparse Convolutions \cite{SparseConv}, of which the volumetric features are then converted to point-level ones; more details about the architectures are given in the supplementary material. Note that for each object instance, $\bm{F}^{\mathcal{Y}_o}$ can be pre-computed during inference for efficiency.

\subsection{Dual Feature Disengagement and Alignment}
\label{subsec:dual_feature_disengagement_and_alignment}

The key to figure out the pose between the object observation and its CAD model lies in the establishment of correspondence. As pointed out in Sec. \ref{sec:Introduction}, there exist at least two ways to achieve this goal: i) learning the partial point set $\widetilde{\bm{P}}^{\mathcal{X}_{o}}$ in object system from complete $\bm{P}^{\mathcal{Y}_{o}}$ to pair with $\bm{P}^{\mathcal{X}_{c}}$, \eg, $(\bm{P}^{\mathcal{X}_{c}},\widetilde{\bm{P}}^{\mathcal{X}_{o}})$, for partial-to-partial correspondence; ii) inferring the complete point set $\widetilde{\bm{P}}^{\mathcal{Y}_{c}}$ in camera coordinate system from partial $\bm{P}^{\mathcal{X}_{c}}$ to pair with $\bm{P}^{\mathcal{Y}_{o}}$, \eg, $(\widetilde{\bm{P}}^{\mathcal{Y}_{c}}, \bm{P}^{\mathcal{Y}_{o}})$,  for complete-to-complete correspondence.

In this paper, we propose to establish the correspondence in the deep feature space, from which \textit{pose feature pairs} along with \textit{match feature pairs} can be generated for the learning of object pose and confidence scores, respectively. Fig. \ref{fig:overview} gives illustrations of the correspondence in both 3D space and feature space.
Specifically, we design a novel \textit{Feature Disengagement and Alignment (FDA) module} to learn the pose feature pairs, \eg, $(\bm{F}^{\mathcal{X}_{c}}_p, \widetilde{\bm{F}}^{\mathcal{X}_{o}}_p)$ and $(\widetilde{\bm{F}}^{\mathcal{Y}_c}_p, \bm{F}^{\mathcal{Y}_o}_p)$ \textit{w.r.t} the above $(\bm{P}^{\mathcal{X}_{c}},\widetilde{\bm{P}}^{\mathcal{X}_{o}})$ and $(\widetilde{\bm{P}}^{\mathcal{Y}_{c}},\bm{P}^{\mathcal{Y}_{o}})$, respectively, and the match feature pairs, \eg, $(\bm{F}^{\mathcal{X}_{c}}_m, \widetilde{\bm{F}}^{\mathcal{X}_{o}}_m)$ and $(\widetilde{\bm{F}}^{\mathcal{Y}_c}_m, \bm{F}^{\mathcal{Y}_o}_m)$, which can be formulated as follows:
\begin{equation}
  \bm{F}^{\mathcal{X}_{c}}_p, \bm{F}^{\mathcal{X}_{c}}_m, \widetilde{\bm{F}}^{\mathcal{X}_{o}}_p, \widetilde{\bm{F}}^{\mathcal{X}_{o}}_m, \widetilde{\bm{P}}^{\mathcal{X}_{o}} = \texttt{FDA}(\bm{F}^{\mathcal{X}_{c}}, \bm{F}^{\mathcal{Y}_{o}}),
\label{EqnPartialFDA}
\end{equation}
\begin{equation}
  \bm{F}^{\mathcal{Y}_o}_p, \bm{F}^{\mathcal{Y}_o}_m,\widetilde{\bm{F}}^{\mathcal{Y}_c}_p, \widetilde{\bm{F}}^{\mathcal{Y}_c}_m, \widetilde{\bm{P}}^{\mathcal{Y}_c} = \texttt{FDA}(\bm{F}^{\mathcal{Y}_o}, \bm{F}^{\mathcal{X}_c}).
  \label{EqnCompleteFDA}
\end{equation}
We term the partial-to-partial (\ref{EqnPartialFDA}) and complete-to-complete (\ref{EqnCompleteFDA}) FDA modules as P2P-FDA and C2C-FDA modules, respectively.

\begin{figure}[t]
  \centering
  \includegraphics[width=0.99\linewidth]{FigFDA.pdf}
    \caption{Illustrations of dual Feature Disengagement and Alignment modules. ``T" denotes matrix transposition, and ``$\times$" denotes matrix multiplication.
    }
    \label{fig:FDA}
\end{figure}

\subsubsection{Feature Disengagement and Alignment Module}

Feature Disengagement and Alignment (FDA) module takes point-wise feature maps of different coordinate systems as inputs, disengages each feature map into pose and match ones, which are then aligned across systems to establish deep correspondence. Fig. \ref{fig:FDA} gives illustrations of both P2P-FDA and C2C-FDA modules, where network specifics are also given.

We take P2P-FDA module (\ref{EqnPartialFDA}) as an example to illustrate the implementation of FDA.
Specifically, as shown in Fig. \ref{fig:FDA}, we firstly disengage  $\bm{F}^{\mathcal{X}_c}$ into a pose feature $\bm{F}^{\mathcal{X}_c}_{p1}$ and a match one $\bm{F}^{\mathcal{X}_c}_{m1}$:
\begin{equation}
\bm{F}^{\mathcal{X}_c}_{p1} = \texttt{MLP}(\bm{F}^{\mathcal{X}_c}), \bm{F}^{\mathcal{X}_c}_{m1} = \texttt{MLP}(\bm{F}^{\mathcal{X}_c}),
\end{equation}
where \texttt{MLP}$(\cdot)$ denotes a subnetwork of Multi-layer Perceptron (MLP).
The same applies to $\bm{F}^{\mathcal{Y}_{o}}$, and we have $\bm{F}^{\mathcal{Y}_{o}}_{p1}$ and $\bm{F}^{\mathcal{Y}_{o}}_{m1}$. The match features $\bm{F}^{\mathcal{X}_c}_{m1}$ and $\bm{F}^{\mathcal{Y}_{o}}_{m1}$ is then used for the learning of an attention map $\bm{A}_1 \in \mathbb{R}^{N_{\mathcal{X}}\times N_{\mathcal{Y}}}$ as follows:
\begin{equation}
  \bm{A}_1 = \texttt{Softmax}(\bm{F}^{\mathcal{X}_c}_{m1} \times \texttt{Transpose}(\bm{F}^{\mathcal{Y}_{o}}_{m1})),
\end{equation}
where \texttt{Transpose}$(\cdot)$ denotes tensor transposition, and \texttt{Softmax}$(\cdot)$ denotes softmax operation along columns. Each element $a_{1,ij}$ in $\bm{A}_1$ indicates the match degree between $i^{th}$ point in $\bm{P}^{\mathcal{X}_c}$ and $j^{th}$ one in $\bm{P}^{\mathcal{Y}_o}$. Then pose and match features of the partial observation $\mathcal{X}_o$ in object system can be interpolated by matrix multiplication of $\bm{A}_1$ and those of $\bm{P}^{\mathcal{Y}_o}$, respectively, to be aligned with features of $\mathcal{X}_c$ in camera coordinate system:
\begin{equation}
  \left\{\begin{array}{l}
    \bm{F}^{\mathcal{X}_{c}}_p =  \bm{F}^{\mathcal{X}_c}_{p1} \\
    \widetilde{\bm{F}}^{\mathcal{X}_{o}}_p =  \bm{A}_1 \times \bm{F}^{\mathcal{Y}_{o}}_{p1}
   \end{array}\right.,
   \left\{\begin{array}{l}
    \bm{F}^{\mathcal{X}_{c}}_m =  \bm{F}^{\mathcal{X}_c}_{m1} \\
    \widetilde{\bm{F}}^{\mathcal{X}_{o}}_m =  \bm{A}_1 \times \bm{F}^{\mathcal{Y}_{o}}_{m1}
   \end{array}\right..
\end{equation}
Through feature alignment, $\widetilde{\bm{P}}^{\mathcal{X}_o}$ is expected to be decoded out from $\widetilde{\bm{F}}^{\mathcal{X}_{o}}_p$:
\begin{equation}
\widetilde{\bm{P}}^{\mathcal{X}_o} = \texttt{MLP}(\widetilde{\bm{F}}^{\mathcal{X}_{o}}_p).
\end{equation}
Supervisions on the reconstruction of $\widetilde{\bm{P}}^{\mathcal{X}_o}$ guide the learning of deep correspondence in P2P-FDA module.

P2P-FDA module (\ref{EqnPartialFDA}) learns deep correspondence of the partial $\mathcal{X}$ in two coordinate systems, while C2C-FDA module (\ref{EqnCompleteFDA}) infers that of the complete $\mathcal{Y}$ via a same network structure, as shown in Fig. \ref{fig:FDA}(b). We adopt dual FDA modules in our design to enable robust correspondence establishment, since they bring complementary functions: P2P-FDA module provides more accurate correspondence than that of C2C-FDA module, due to the difficulty in shape completion from partial observation for the latter module; however, C2C-FDA module plays a vital role under the condition of severe occlusions, which P2P-FDA module can hardly handle with.

\subsection{Confidence-based Pose Estimation}
\label{subsec:PoseEstimation}
After dual feature disengagement and alignment, we construct the pose and match feature pairs as follows:
\begin{equation}
  \bm{F}_p = \begin{bmatrix}
    \bm{F}^{\mathcal{X}_c}_p, & \widetilde{\bm{F}}^{\mathcal{X}_o}_p\\
    \widetilde{\bm{F}}^{\mathcal{Y}_c}_p,& \bm{F}^{\mathcal{Y}_o}_p
   \end{bmatrix},  \bm{F}_m = \begin{bmatrix}
    \bm{F}^{\mathcal{X}_c}_m, & \widetilde{\bm{F}}^{\mathcal{X}_o}_m\\
    \widetilde{\bm{F}}^{\mathcal{Y}_c}_m,& \bm{F}^{\mathcal{Y}_o}_m
   \end{bmatrix}.
\end{equation}
As shown in Fig. \ref{fig:overview}, the paired match feature $\bm{F}_m$ is fed into an MLP for the learning of confidence scores $\bm{s}=\{s_i\}_{i=1}^{N_{\mathcal{X}}+N_{\mathcal{Y}}}$ to reflect the qualities of deep correspondence:
\begin{equation}
  \bm{s} = \texttt{MLP}(\bm{F}_m).
\end{equation}

The paired pose feature $\bm{F}_p$ is also fed into an MLP and weighted by $\bm{s}$ for precisely estimating the 6D pose $(\bm{R}, \bm{t})$:
\begin{eqnarray}
  &\bm{R} = \texttt{MLP}(\bm{f}), \bm{t} = \texttt{MLP}(\bm{f}), \\
  \textit{s.t.} &\bm{f} = \texttt{SUM}(\texttt{SoftMax}(\bm{s}) \cdot \texttt{MLP}(\bm{F}_p)), \notag
\end{eqnarray}
where \texttt{SUM} denotes summation along rows.

Rather than numerical calculation from two paired point sets, we directly regress the 6D object pose from deep pair-wise features with confidence scores, which effectively weakens the negative impact of correspondence of low quality on pose estimation, and thus realizes more precise results.

\subsection{Training of Deep Correspondence Learning Network}
\label{subsec:training_objectives}

For dual FDA modules, we supervise the reconstruction of $\widetilde{\bm{P}}^{\mathcal{X}_o}=\{\widetilde{\bm{p}}_i^{\mathcal{X}_o}\}_{i=1}^{N_{\mathcal{X}}}$ and $\widetilde{\bm{P}}^{\mathcal{Y}_c}=\{\widetilde{\bm{p}}_i^{\mathcal{Y}_c}\}_{i=1}^{N_{\mathcal{Y}}}$ to guide the learning of deep correspondence via the following objectives:
\begin{equation}
\mathcal{L}_{p2p} = \frac{1}{N_{\mathcal{X}}} \sum_{i=1}^{N_{\mathcal{X}}} || \widetilde{\bm{p}}_i^{\mathcal{X}_o} - \bm{R}^{*T}(\bm{p}_i^{\mathcal{X}_c}-\bm{t}^*) ||,
\label{EqnLossX}
\end{equation}
\begin{equation}
\mathcal{L}_{c2c} = \frac{1}{N_{\mathcal{Y}}} \sum_{i=1}^{N_{\mathcal{Y}}} || \widetilde{\bm{p}}_i^{\mathcal{Y}_c} - (\bm{R}^*\bm{p}_i^{\mathcal{Y}_o}+\bm{t}^*) ||,
\label{EqnLossY}
\end{equation}
where $\bm{P}^{\mathcal{X}_c}=\{\bm{p}_i^{\mathcal{X}_c}\}_{i=1}^{N_{\mathcal{X}}}$ and $\bm{P}^{\mathcal{Y}_o}=\{\bm{p}_i^{\mathcal{Y}_o}\}_{i=1}^{N_{\mathcal{Y}}}$ are input point sets, and $\bm{R}^*$ and $\bm{t}^*$ denote ground truth 6D pose.
For the confidence-based pose estimation, we use the following objectives on top of the learning of the predicted object pose $(\bm{R}, \bm{t})$ and confidence scores $\bm{s}=\{s_i\}_{i=1}^{N_{\mathcal{X}}+N_{\mathcal{Y}}}$, respectively:
\begin{equation}
  \mathcal{L}_{pose} = \frac{1}{N_{\mathcal{Y}}} \sum_{i=1}^{N_{\mathcal{Y}}} ||\bm{R}\bm{p}_i^{\mathcal{Y}_o}+\bm{t}- (\bm{R}^*\bm{p}_i^{\mathcal{Y}_o}+\bm{t}^*) ||.
\label{EqnLossPose}
\end{equation}
\begin{eqnarray}
  \mathcal{L}_{conf} = \frac{1}{N_{\mathcal{X}}} \sum_{i=1}^{N_{\mathcal{X}}} \sigma(|| \widetilde{\bm{p}}_i^{\mathcal{X}_o} - \bm{R}^{T}(\bm{p}_i^{\mathcal{X}_c}-\bm{t})  ||,  s_i)  \notag \\
    + \frac{1}{N_{\mathcal{Y}}} \sum_{j=1}^{N_{\mathcal{Y}}} \sigma(|| \widetilde{\bm{p}}_j^{\mathcal{Y}_c} - (\bm{R}\bm{p}_j^{\mathcal{Y}_o}+\bm{t})|| ,  s_{N_{\mathcal{X}}+j}),
\label{EqnLossConf}
\end{eqnarray}
where $\sigma(d,s)=ds-wlog(s)$, and $w$ is a balancing hyperparameter. We note that the objectives (\ref{EqnLossX}), (\ref{EqnLossY}) and (\ref{EqnLossPose}) are designed for asymmetric objects, while for symmetric ones, we modify them by replacing $L_2$ distance with Chamfer distance, as done in \cite{densefusion}.

The overall training objective combines (\ref{EqnLossX}), (\ref{EqnLossY}), (\ref{EqnLossPose}), and (\ref{EqnLossConf}), resulting in the following optimization problem:
\begin{equation}
  \min \mathcal{L} =  \lambda_1 \mathcal{L}_{p2p} + \lambda_2 \mathcal{L}_{c2c} + \lambda_3 \mathcal{L}_{pose} + \lambda_4 \mathcal{L}_{conf},
\label{EqnLossALL}
\end{equation}
where $\lambda_1$, $\lambda_2$, $\lambda_3$ and $\lambda_4$ are penalty parameters.

\subsection{Confidence-based Pose Refinement}
\label{subsec:confidence_based_pose_refinement}

\begin{figure}[t]
  \centering
  \includegraphics[width=0.99\linewidth]{FigRefinement2.pdf}
    \caption{An illustration of the iterative confidence-based pose estimation network.
    }
    \label{fig:refinement}
\end{figure}

To take full advantages of the learned correspondence, we propose a confidence-based pose refinement network, as shown in Fig. \ref{fig:refinement}, where the input point set $\bm{P}^{\mathcal{X}_c}$ is transformed with predicted pose, and paired with $\widetilde{\bm{F}}^{\mathcal{X}_o}_p$ for residual pose estimation in an iterative manner. Specifically, assuming after $k-1$ iterations of refinement, the current object pose is updated as $(\bm{R}_{k-1}, \bm{t}_{k-1})$, and we use it for transforming $\bm{P}^{\mathcal{X}_c}=\{\bm{p}_i^{\mathcal{X}_c}\}_{i=1}^{N_{\mathcal{X}}}$ to $\bm{P}_{k-1}^{\mathcal{X}_c}=\{\bm{R}_{k-1}^{T}(\bm{p}_i^{\mathcal{X}_c}-\bm{t}_{k-1})\}_{i=1}^{N_{\mathcal{X}}}$; for forming pair-wise pose features with the learned correspondence in dual FDA modules, we reuse $\widetilde{\bm{F}}^{\mathcal{X}_o}_p$ by concatenating it with $\bm{P}_{k-1}^{\mathcal{X}_c}$. Similarly to Sec. \ref{subsec:PoseEstimation}, we feed the pose feature pairs into an MLP, and weight them by reusing the confidence scores  $\bm{s}_{N_\mathcal{X}}$ (denoting the first $N_\mathcal{X}$ elements of $\bm{s}$) for estimating the residual pose $(\Delta\bm{R}_{k}, \Delta\bm{t}_{k})$:
\begin{eqnarray}
  &\Delta\bm{R}_{k} = \texttt{MLP}(\bm{f}_k), \Delta\bm{t}_{k} = \texttt{MLP}(\bm{f}_k), \\
  \textit{s.t.} &\bm{f}_k = \texttt{SUM}(\texttt{SoftMax}(\bm{s}_{N_\mathcal{X}}) \cdot \texttt{MLP}([\bm{P}_{k-1}^{\mathcal{X}_c}, \widetilde{\bm{F}}^{\mathcal{X}_o}_p])). \notag
\end{eqnarray}
Finally, the pose $(\bm{R}_{k}, \bm{t}_{k})$ of the $k^{th}$ iteration can be obtained as follows:
\begin{equation}
  \bm{R}_k = \Delta\bm{R}_k \bm{R}_{k-1}, \bm{t}_k = \bm{R}_{k-1}\Delta\bm{t}_{k} + \bm{t}_{k-1}.
\end{equation}

\section{Experiments}
\label{exp:Exp}

\subsubsection{Datasets} We conduct experiments on three benchmarking datasets, including YCB-Video \cite{YCB}, LineMOD \cite{LineMod}, and Occlusion-LineMOD \cite{learning6d}.
YCB-Video dataset consists of $92$ RGB-D videos with $21$ different object instances, fully annotated with object poses and masks. Following \cite{densefusion}, we use $80$ videos therein for training along with additional $80,000$ synthetic images, and evaluate DCL-Net on $2,949$ keyframes sampled from the rest $12$ videos.
LineMOD is also a fully annotated dataset for 6D pose estimation, containing $13$ videos with $13$ low-textured object instances; we follow the prior work \cite{densefusion} to split training and testing sets.
Occlusion-LineMOD is an annotated subset of LineMOD with 8 different object instances, which handpicks RGB-D images of scenes with heavy object occlusions and self-occlusions from LineMOD, making the task of pose estimation more challenging; following \cite{hybridpose}, we use the DCL-Net trained on the original LineMOD to evaluate on Occlusion-LineMOD.

\subsubsection{Implementation Details} For both object observations and CAD models, we sample point sets with $1,024$ points as inputs of DCL-Net; that is, $N_{\mathcal{X}}=N_{\mathcal{Y}}=1,024$. For the training objectives, we set the penalty parameters $\lambda_1$, $\lambda_2$, $\lambda_3$, $\lambda_4$ in (\ref{EqnLossALL}) as $5.0$, $1.0$, $1.0$, and $1.0$, respectively; $w$ in (\ref{EqnLossConf}) is set as $0.01$. During inference, we run twice the confidence-based pose refinement for improvement of pose precision.

\subsubsection{Evaluation Metrics} We use the same evaluation metrics as those in \cite{densefusion}. For YCB-Video dataset, the average closest point distance (ADD-S) \cite{posecnn} is employed to measure the pose error; following \cite{densefusion}, we report the Area Under the Curve (AUC) of ADD-S with the maximum threshold at $0.1$m, and the percentage of ADD-S smaller than the minimum tolerance at $2$cm ($<2$cm). For both LineMOD and Occlusion-LineMOD datasets, ADD-S is employed only for symmetric objects, while the Average Distance (ADD) for asymmetric objects; we report the percentage of distance smaller than $10\%$ of object diameter. Besides, we use Chamfer Distance (CD) to measure the reconstruction results.

\subsection{Ablation Studies and Analyses}

We firstly conduct ablation studies to evaluate the efficacy of novel designs proposed in our DCL-Net. These experiments are conducted on YCB-Video dataset \cite{YCB}.

\begin{table}[t]
  \begin{center}
    \caption{Ablation studies of the use of dual FDA modules on YCB-Video dataset \cite{YCB}. Experiments are conducted without confidence-based weighting and pose refinement. }
    \label{tab:AblationFDA}
      \begin{tabular}{M{2cm}|M{2cm}|M{1.3cm}M{1.3cm}M{1.3cm}M{1.3cm}}
        \hline
        \multirow{2}{*}{P2P-FDA} & \multirow{2}{*}{C2C-FDA} &  \multirow{2}{*}{AUC}  &  \multirow{2}{*}{$<2$cm} & \multicolumn{2}{c}{CD ($\times {10}^{-3}$)}\\
        \cline{5-6}
        & & & & $\bm{P}^{\mathcal{X}_o}$ & $\bm{P}^{\mathcal{Y}_c}$ \\
        \hline
        $\times$ & $\times$ & 94.1 & 97.4 & $-$ & $-$\\
        \checkmark & $\times$  & 95.0 & 98.7 & 7.1 & $-$\\
        $\times$ & \checkmark  & 94.5 & 98.8 & $-$ & 8.2\\
        \checkmark & \checkmark  & $\mathbf{95.3}$ & $\mathbf{99.0}$& $\mathbf{7.0}$ & $\mathbf{8.1}$ \\
        \hline
      \end{tabular}
    \end{center}
\end{table}

\subsubsection{Effects of Dual Feature Disengagement and Alignment}  We conduct four experiments to evaluate the efficacy of the use of dual FDA modules: i) without any FDA modules (baseline), ii) only with P2P-FDA, iii) only with C2C-FDA, and iv) with dual modules. For simplicity, these experiments are conducted without confidence-based weighting as well as pose refinement. The quantitative results on ADD-S AUC and ADD-S$<2$cm are shown in Table \ref{tab:AblationFDA}, where the reconstruction results of asymmetric objects are also reported. From the table, methods with (one or dual) FDA modules indeed outperforms the baseline, which demonstrates the importance of deep correspondence learning on pose estimation. Single P2P-FDA module achieves more accurate results than single C2C-FDA module by making better reconstructions ($7.1\times 10^{-3}$ versus $8.2 \times 10^{-3}$ on CD) and deep correspondence as well, and the mixed use of them boosts the performance, indicating their complementary advantages. For the last framework, we visualize the reconstruction results along with the learned correspondence of both P2P-FDA and C2C-FDA modules in Fig. \ref{fig:resutls_fda}; shape completion can be achieved for C2C-FDA module, even with severe occlusions, to build valid deep correspondence of high quality, and thus make DCL-Net more robust and reliable.

\begin{figure}[t]
  \centering
  \includegraphics[width=1.0\linewidth]{Fig_vis_attention_reconstruction.pdf}
    \caption{Visualizations of shape predictions, attentions, and correspondence of both P2P-FDA and C2C-FDA modules on YCB-Video dataset \cite{YCB}. Best view in electronic version.}
    \label{fig:resutls_fda}
\end{figure}

\begin{table}[t]
  \begin{center}
    \caption{Quantitative results obtained by least-squares optimization \cite{arun1987least} and our proposed direct regression on YCB-Video dataset \cite{YCB}. Experiments are conducted without pose refinement. }
    \label{tab:AblationConf}
      \begin{tabular}{M{2cm}|M{5cm}|M{1.5cm}M{1.5cm}}
      \hline
        &  & AUC & $<2$cm  \\
      \hline
      \multirow{2}{*}{w/o Conf.} & Least-squares Optimization \cite{arun1987least} & 94.7 & 98.2 \\
      & Direct Pose Regression & 95.3 & 99.0 \\
      \hline
      \multirow{2}{*}{with Conf.} & Least-squares Optimization \cite{arun1987least} & 95.4 & 98.3\\
      & Direct Pose Regression & $\mathbf{95.8}$ & $\mathbf{99.0}$ \\
      \hline
      \end{tabular}
    \end{center}
\end{table}

We also explore the attention maps of dual FDA modules in Fig. \ref{fig:resutls_fda}. Take C2C-FDA module as an example, the predicted points are learned from the features of the input observed ones via attention maps, \textit{i.e.}, each predicted point corresponds to the observed ones with different attention weights, and we thus colorize those corresponding points with large weights in Fig. \ref{fig:resutls_fda}; as shown in the figure, for the predicted points (red) locate at the observed parts, most of the input points with larger weights (red) could locate at the corresponding local regions, showing the qualities of attention maps, while for those at the occluded parts (blue), the corresponding points (blue) may locate scatteredly, but thanks to the correspondence learning in feature space, these points could still be completed in the C2C-FDA reconstruction results.

\subsubsection{Effects of Confidence-based Pose Estimation} Through learning deep correspondence in feature space, DCL-Net achieves direct regression of object poses, while the predictions of dual FDA modules can also establish point correspondence \textit{w.r.t} inputs to solve poses via least-squares optimization \cite{arun1987least}. We compare the quantitative results obtained by these two approaches (without pose refinement) in Table \ref{tab:AblationConf}, where results of direct regression from deep feature correspondence outperforms those from point correspondence consistently with or without confidence scores,  showing that pose estimation from feature space is less sensitive to the correspondence of low qualities, thanks to the direct objectives for the end task. Besides, we also observe that the learning of confidence scores not only measures the qualities of correspondence and decreases the influence of bad correspondence, but also helps improve the qualities themselves effectively.

\begin{table}[t]
  \caption{Quantitative results of DCL-Net with or without pose refinement on YCB-Video dataset \cite{YCB}. }
  \label{tab:AblationRefine}
  \begin{center}
      \begin{tabular}{M{4cm}|M{1.5cm}M{1.5cm}}
        \hline
          & AUC & $<2$cm \\
        \hline
        w/o Pose Refinement  & 95.8 & 99.0\\
        with Pose Refinement  & $\mathbf{96.6}$ & $\mathbf{99.0}$\\
        \hline
      \end{tabular}
    \end{center}
\end{table}

\begin{figure}[t]
  \centering
  \includegraphics[width=1.0\linewidth]{FigRefine2.pdf}
    \caption{Qualitative results of DCL-Net with or without pose refinement on YCB-Video dataset \cite{YCB}. The sampled points of CAD models are transformed by the predicted poses and projected to 2D images.}
    \label{fig:results_refinement}
\end{figure}

\subsubsection{Effects of Confidence-based Pose Refinement} Table \ref{exp:sota_ycb} demonstrates the efficiency of our confidence-based pose refinement for boosting the performance, \eg, improvement by $0.8\%$ on the metric of ADD-S AUC, which is also verified by the qualitative results shown in Fig. \ref{fig:results_refinement}.

\subsection{Comparisons with Existing Methods}

\begin{table}[t]
  \centering
  \caption{Quantitative results of different methods on YCB-Video dataset \cite{YCB}. The evaluation metrics are ADD-S AUC and ADD-S$<2$cm. Objects with bold name are symmetric. }
  \label{exp:sota_ycb}
  \resizebox{0.99\textwidth}{!}{
  \begin{tabular}{l|cc|cc|c|cc|cc|cc|cc}
  \toprule
                           & \multicolumn{2}{c|}{PoseCNN+ICP\cite{posecnn}} & \multicolumn{2}{c|}{DenseFusion\cite{densefusion}} & G2L\cite{G2L}   & \multicolumn{2}{c|}{PVN3D\cite{pvn3d}} & \multicolumn{2}{c|}{PR-GCN\cite{PRGCN}}  & \multicolumn{2}{c|}{FFB6D}          & \multicolumn{2}{c}{DCL-Net}    \\
                           & AUC       & $\textless$2cm                     & AUC        & $\textless$2cm                        & AUC             & AUC              & $\textless$2cm      & AUC              & $\textless$2cm        & AUC             & $\textless$2cm    & AUC     & $\textless$2cm   \\
  \midrule
  002\_master\_chef\_can   & 95.8      & \textbf{100.0}                     & 96.4       & \textbf{100.0}                        & 94.0            & 96.0             & \textbf{100.0}      & \textbf{97.1}    & \textbf{100.0}        & 96.3            & \textbf{100.0}       & 96.1            & \textbf{100.0} \\
  003\_cracker\_box        & 92.7      & 91.6                               & 95.5       & 99.5                                  & 88.7            & 96.1             & \textbf{100.0}      & \textbf{97.6}    & \textbf{100.0}        & 96.3            & \textbf{100.0}                & 96.4            & 99.4           \\
  004\_sugar\_box          & 98.2      & \textbf{100.0}                     & 97.5       & \textbf{100.0}                        & 96.0            & 97.4             & \textbf{100.0}      & \textbf{98.3}    & \textbf{100.0}        & 97.6            & \textbf{100.0}       & 98.1            & \textbf{100.0} \\
  005\_tomato\_soup\_can   & 94.5      & 96.9                               & 94.96      & 96.9                                  & 86.4            & \textbf{96.2}    & \textbf{98.1}       & 95.3             & 97.6                  & 95.6            & 98.2                           & 95.8            & 97.7           \\
  006\_mustard\_bottle     & 98.6      & \textbf{100.0}                     & 97.2       & \textbf{100.0}                        & 95.9            & 97.5             & \textbf{100.0}      & 97.9             & \textbf{100.0}        & 97.8            & \textbf{100.0}       & \textbf{98.7}   & \textbf{100.0} \\
  007\_tuna\_fish\_can     & 97.1      & \textbf{100.0}                     & 96.6       & \textbf{100.0}                        & 84.1            & 96.0             & \textbf{100.0 }     & 97.6             & \textbf{100.0}        & 96.8            & \textbf{100.0}       & 97.4            & \textbf{100.0} \\
  008\_pudding\_box        & 97.9      & \textbf{100.0}                     & 96.5       & \textbf{100.0}                        & 93.5            & 97.1             & \textbf{100.0}      & \textbf{98.4}    & \textbf{100.0}        & 97.1            & \textbf{100.0}       & 98.2            & \textbf{100.0} \\
  009\_gelatin\_box        & 98.8      & \textbf{100.0}                     & 98.1       & \textbf{100.0}                        & 96.8            & 97.7             & \textbf{100.0}      & 96.2             & 94.4                  & 98.1            & \textbf{100.0}    & \textbf{98.9}   & \textbf{100.0} \\
  010\_potted\_meat\_can   & 92.7      & 93.6                               & 91.3       & 93.1                                  & 86.2            & 93.3             & 94.6                & \textbf{96.6}    & \textbf{99.1}         & 94.7            & 94.3                   & 93.1            & 94.7           \\
  011\_banana              & 97.1      & 99.7                               & 96.6       & \textbf{100.0}                        & 96.3            & 96.6             & \textbf{100.0}      & \textbf{98.5}    & \textbf{100.0}        & 97.2            & \textbf{100.0}     & 98.1            & \textbf{100.0} \\
  019\_pitcher\_base       & 97.8      & \textbf{100.0}                     & 97.1       & \textbf{100.0}                        & 91.8            & 97.4             & \textbf{100.0}      & 98.1             & \textbf{100.0}        & 97.6            & \textbf{100.0}           & 98.0            & 99.8            \\
  021\_bleach\_cleanser    & 96.9      & 99.4                               & 95.8       & \textbf{100.0}                        & 92.0            & 96.0             & \textbf{100.0}      & \textbf{97.9}    & \textbf{100.0}        & 96.8            & \textbf{100.0}       & 97.0            & \textbf{100.0} \\
  \textbf{024\_bowl}       & 81.0      & 54.9                               & 88.2       & 98.8                                  & 86.7            & 90.2             & 80.5                & 90.3             & 96.6                  & 96.3            & \textbf{100.0}        & \textbf{97.3}   & \textbf{100.0} \\
  025\_mug                 & 95.0      & 99.8                               & 97.1       & \textbf{100.0}                        & 95.4            & 97.6             & \textbf{100.0}      & \textbf{98.1}    & \textbf{100.0}        & 97.3            & \textbf{100.0}             & 97.8            & \textbf{100.0} \\
  035\_power\_drill        & \textbf{98.2}      & 99.6                      & 96.0       & 98.7                                  & 95.2            & 96.7             & \textbf{100.0}      & 98.1             & \textbf{100.0}        & 97.2            & \textbf{100.0}        & 98.0            & \textbf{100.0} \\
  \textbf{036\_wood\_block}& 87.6      & 80.2                               & 89.7       & 94.6                                  & 86.2            & 90.4             & 93.8                & \textbf{96.0}    & \textbf{100.0}        & 92.6            & 92.1                 & 93.9            & 97.5           \\
  037\_scissors            & 91.7      & 95.6                               & 95.2       & \textbf{100.0 }                       & 83.8            & 96.7             & \textbf{100.0}      & \textbf{96.7}    & \textbf{100.0  }      & 97.7            & \textbf{100.0}       & 87.6            & 98.3           \\
  040\_large\_marker       & 97.2      & 99.7                               & 97.5       & \textbf{100.0   }                     & 96.8            & 96.7             & 99.8                & 97.9             & \textbf{100.0}        & 96.6            & \textbf{100.0}      & 97.8            & 99.8           \\
  \textbf{051\_large\_clamp}        & 75.2      & 74.9                      & 72.9       & 79.2                                  & 94.4            & 93.6             & 93.6                & 87.5             & 93.3                  & 96.8            & \textbf{100.0}         & 95.7            & 98.6           \\
  \textbf{052\_extra\_large\_clamp} & 64.4      & 48.8                      & 69.8       & 76.3                                  & \textbf{92.3}   & 88.4             & 83.6                & 79.7             & 84.6                  & \textbf{96.0}   & \textbf{98.6}        & 88.8            & 87.2           \\
  \textbf{061\_foam\_brick}         & 97.2      & \textbf{100.0}            & 92.5       & \textbf{100.0}                        & 94.7            & 96.8             & \textbf{100.0}      & \textbf{97.8}    & \textbf{100.0}        & 97.3            & \textbf{100.0}        & 97.5            & \textbf{100.0} \\
  \midrule
  MEAN                     & 93.0      & 93.2                               & 93.1       & 96.8                                  & 92.4            & 95.5             & 97.6                & 95.8             & 98.5                  & \textbf{96.6}   & \textbf{99.2}         & \textbf{96.6}   & 99.0           \\
  \bottomrule

  \end{tabular}}
\end{table}

\begin{figure}[!h]
  \centering
  \includegraphics[width=1.0\linewidth]{FigYCBSOTA2.pdf}
    \caption{Qualitative results of different methods on YCB-Video dataset \cite{YCB}. The sampled points of CAD models are transformed by the predicted poses and projected to 2D images.}
    \label{fig:exp_ycb}
\end{figure}

We compare our proposed DCL-Net with the existing methods for 6D object pose estimation from RGB-D data, including those based on direct regression (\eg, DenseFusion \cite{densefusion} and PR-GCN \cite{PRGCN}), and those based on dense correspondence learning (\eg, PVN3D \cite{pvn3d} and FFB6D\cite{FFB6D}).

Quantitative results on the three benchmarking datasets, including YCB-Video \cite{YCB}, LineMOD \cite{LineMod}, and Occlusion-LineMOD \cite{learning6d}, are reported in Table \ref{exp:sota_ycb}, Table \ref{exp:sota_linemod}, and Table \ref{exp:sota_LMO}, respectively, all of which show the superiority of our DCL-Net consistently in the regime of pose precision;  qualitative results on YCB-Video dataset \cite{YCB} are also provided in Fig. \ref{fig:exp_ycb} to verify the advantages of our DCL-Net. Remarkably, on the more challenging Occlusion-LineMOD dataset, the improvements of our DCL-Net over the state-of-the-art methods of PR-GCN \cite{PRGCN} and FFB6D \cite{FFB6D} reach $5.6\%$ and $4.4\%$ on the metric of ADD(S), respectively, indicating the advantages of our DCL-Net on handling with object occlusions or self-occlusions.

\begin{table}[t]
  \centering
  \caption{Quantitative results of different methods on ADD(S) on LineMOD dataset \cite{LineMod}. Objects with bold name are symmetric.}
  \label{exp:sota_linemod}
  \resizebox{1.0\textwidth}{!}{
  \begin{tabular}{l|ccccccc|c}
  \toprule
          & \begin{tabular}[c]{@{}c@{}}Implicit\\ +ICP\cite{implicit}\end{tabular} & \begin{tabular}[c]{@{}c@{}}SSD6D\\ +ICP\cite{ssd6d}\end{tabular} & \begin{tabular}[c]{@{}c@{}}PointFusion\\\cite{pointfusion}\end{tabular} & \begin{tabular}[c]{@{}c@{}}DenseFusion\\\cite{densefusion}\end{tabular} & \begin{tabular}[c]{@{}c@{}}DenseFusion\\ (Iterative)\cite{densefusion}\end{tabular} & \begin{tabular}[c]{@{}c@{}}G2L\\\cite{G2L}\end{tabular} & \begin{tabular}[c]{@{}c@{}}PR-GCN\\ \cite{PRGCN}\end{tabular}  & DCL-Net \\
  \midrule
  ape     & 20.6  & 65   & 70.4 & 79.5 & 92.3  & 96.8     & \textbf{97.6}    & 97.4\\
  bench   & 64.3  & 80   & 80.7 & 84.2 & 93.2  & 96.1     & 99.2    & \textbf{99.4}\\
  camera  & 63.2  & 78   & 60.8 & 76.5 & 94.4  & 98.2     & 99.4    & \textbf{99.8}\\
  can     & 76.1  & 86   & 61.1  & 86.6 & 93.1  & 98.0     & 98.4   & \textbf{99.9}\\
  cat     & 72.0  & 70   & 79.1  & 88.8 & 96.5  & 99.2     & 98.7   & \textbf{100.0}\\
  driller & 41.6  & 73 & 47.3   & 77.7  & 87.0  & 99.8     & 98.8   & \textbf{99.9}\\
  duck    & 32.4  & 66   & 63.0  & 76.3  & 92.3 & 97.7     & \textbf{98.9}   & 98.4\\
  \textbf{egg}     & 98.6 & \textbf{100}  & 99.9  & 99.9   & 99.8    & \textbf{100.0}    & 99.9   & \textbf{100.0}\\
  \textbf{glue}    & 96.4   & \textbf{100}  & 99.3 & 99.4  & \textbf{100.0}   & \textbf{100.0}    & \textbf{100.0}        & 99.9\\
  hole    & 49.9   & 49   & 71.8  & 79.0  & 92.1   & 99.0     & 99.4        & \textbf{100.0}\\
  iron    & 63.1  & 78    & 83.2  & 92.1  & 97.0   & 99.3     & 98.5        & \textbf{100.0}\\
  lamp    & 91.7  & 73  & 62.3  & 92.3  & 95.3  & \textbf{99.5}     & 99.2        & \textbf{99.5}\\
  phone   & 71.0  & 79  & 78.8  & 88.0  & 92.8  & 98.9     & 98.4        & \textbf{99.7}\\
  \midrule
  MEAN    & 64.7  & 79  & 73.7  & 86.2 & 94.3   & 98.7     & 98.9       & \textbf{99.5}\\
  \bottomrule
  \end{tabular} }
  \end{table}

\begin{table}[t]
    \centering
    \caption{Quantitative results of different methods on ADD(S) on Occlusion-LineMOD dataset \cite{learning6d}. Objects with bold name are symmetric.}
  \label{exp:sota_LMO}
  \resizebox{1.0\textwidth}{!}{
  \begin{tabular}{l|ccccccccc|c}
  \toprule
                    &  \makecell[c]{PoseCNN \\ \cite{posecnn}} & \makecell[c]{Deep- \\ Heat\cite{deepheat}} & \makecell[c]{SS \\ \cite{ss}}  & \makecell[c]{Pix2pose \\ \cite{pix2pose}} & \makecell[c]{PVNet\\\cite{pvnet}} & \makecell[c]{Hybrid-\\Pose\cite{hybridpose}} & \makecell[c]{PVN3D\\\cite{pvn3d}} & \makecell[c]{PR-GCN\\\cite{PRGCN}}  & \makecell[c]{FFB6D\\\cite{FFB6D}}  & DCL-Net          \\
  \midrule
  ape              &  9.6                                      & 12.1                                       & 17.6                           & 22.0                                      & 15.8                              & 20.9                                         & 33.9                              & 40.2                                & 47.2                                & \textbf{56.7} \\
  can              &  45.2                                     & 39.9                                       & 53.9                           & 44.7                                      & 63.3                              & 75.3                                         & \textbf{88.6}                     & 76.2                                & 85.2                                & 80.2          \\
  cat              &  0.9                                      & 8.2                                        & 3.3                            & 22.7                                      & 16.7                              & 24.9                                         & 39.1                              & \textbf{57.0}                       & 45.7                                & 48.1          \\
  driller          &  41.4                                     & 45.2                                       & 62.4                           & 44.7                                      & 65.7                              & 70.2                                         & 78.4                              & \textbf{82.3}                       & 81.4                                & 81.4          \\
  duck             &  19.6                                     & 17.2                                       & 19.2                           & 15.0                                      & 25.2                              & 27.9                                         & 41.9                              & 30.0                                & \textbf{53.9}                       & 44.6          \\
  \textbf{egg}     &  22.0                                     & 22.1                                       & 25.9                           & 25.2                                      & 50.2                              & 52.4                                         & 80.9                              & 68.2                                & 70.2                                & \textbf{83.6} \\
  \textbf{glue}    &  38.5                                     & 35.8                                       & 39.6                           & 32.4                                      & 49.6                              & 53.8                                         & 68.1                              & 67.0                                & 60.1                                & \textbf{79.1} \\
  hole             &  22.1                                     & 36.0                                       & 21.3                           & 49.5                                      & 39.7                              & 54.2                                         & 74.7                              & \textbf{97.2}                       & 85.9                                & 91.3          \\
  \midrule
  MEAN             &  24.9                                     & 27.0                                       & 27.0                           & 32.0                                      & 40.8                              & 47.5                                         & 63.2                              & 65.0                                & 66.2                                & \textbf{70.6} \\
  \bottomrule
  \end{tabular}
  }
\end{table}

~\\

\noindent\textbf{Acknowledgements.} This work is supported in part by Guangdong R$\&$D key project of China (No.: 2019B010155001), and the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (No.: 2017ZT07X183).  We also thank Yi Li and Xun Xu for their valuable comments.

\newpage

\bibliographystyle{splncs04}

redpajama_set_name: RedPajamaArXiv


text:
package ru.job4j;

import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.boot.builder.SpringApplicationBuilder;
import org.springframework.boot.web.servlet.support.SpringBootServletInitializer;
import org.springframework.context.annotation.ComponentScan;

/**
 * Собирать командой: mvn clean package
 * Запускать командой: java -jar ./target/%локальное_имя_файла%.war
 * По умолчанию контекст приложения - это корень вэб-вервера.
 * Поэтому приложение открывается по адресу http://localhost:8080/
 */
@SpringBootApplication
public class Application extends SpringBootServletInitializer {
    /**
     * Главный метод. Точка входа в приложение.
     * @param args массив аргументов запуска.
     */
    public static void main(String[] args) {
        SpringApplication.run(Application.class, args);
    }

    @Override
    protected SpringApplicationBuilder configure(SpringApplicationBuilder application) {
        return application.sources(Application.class);
    }
}

redpajama_set_name: RedPajamaGithub


text:
Highway 33 closed north of Ventura after crash knocks down utility lines
Highway 33 was closed in both directions Tuesday morning, just north of Ventura, after a crash reportedly knocked down utility lines late Monday.
Highway 33 closed north of Ventura after crash knocks down utility lines Highway 33 was closed in both directions Tuesday morning, just north of Ventura, after a crash reportedly knocked down utility lines late Monday. Check out this story on vcstar.com: https://www.vcstar.com/story/news/2019/01/15/highway-33-closed-north-ventura-crash-utility-lines/2578889002/
Christian Martinez, Ventura County Star Published 7:03 a.m. PT Jan. 15, 2019 | Updated 8:59 a.m. PT Jan. 15, 2019
Highway 33 was closed in both directions Tuesday morning, just north of Ventura, after a crash reportedly knocked down utility lines late Monday, the California Highway Patrol said.
The non-injury crash was reported just after 11:10 p.m. Monday, north of Casitas Vista Road.
As of 4 a.m. Tuesday, both lanes of the 33 were closed due to lines down in the roadway, CHP officials said.
MORE: First of series of storms rolls into Ventura County, closes portions of PCH, Highway 33
Southbound traffic was closed at Santa Ana Boulevard and Larmier Avenue while northbound traffic was being diverted off the roadway at Casitas Vista to Santa Ana, officials said.
Utility personnel had been requested at the scene and were expected to arrive around 10 a.m., CHP officials said.
CHP responded to the scene along with Ventura County Sheriff's Office and Ventura County Fire Department personnel.
MORE: Driver who crashed in Oxnard, critically injuring two passengers, arrested on suspicion of DUI
Read or Share this story: https://www.vcstar.com/story/news/2019/01/15/highway-33-closed-north-ventura-crash-utility-lines/2578889002/
Ventura police arrest suspect in fatal shooting
Man allegedly dumps gun while fleeing from police
Oxnard's new free clinic needed 'desperately'


text:
1998 Lotus Elise Sport 190
SCTGA1117WHE34016
Calypso Red
One of less than 25 Sport 190 models imported
*One of fewer than 25 imported
*Less than 500 original miles
*Lowest mileage example known to exist
*Non-street legal; track-day or race only
*The lightest of all the Lotus Elise models
The Lotus Elise is a two-seat, mid-engined roadster initially released in 1996. The Elise has a fiberglass body atop its aluminum chassis that provides a stiff platform for the suspension while keeping weight to a minimum.
The Sport 190 model is a competition-only (not street legal) version imported to the United States in 1998. Fewer than 25 made the trip across the pond. Upgrades to the standard Elise included a roll cage, fire system, racing seats, master electrical switch, quick-release steering wheel, stiffer racing suspension and five-point racing harness, among others. It's powered by a highly-tuned version of the Rover 1.8-liter four-cylinder producing 190 hp at 7,000 rpm. The 1,485 pound Sport 190 hits sixty mph in 4.3 seconds and 100 mph in 10.5 seconds.
This Series 1 Sport 190 model was originally purchased on September 24, 1998 for $55,600. We bought the Lotus in 2014 from the original owner, who lightly tracked the car early in its life.
sales@scdgarage.com
Latest Inventory Updates and More!


text:
Board index » COMMUNITY: The Home » ARTS: Oshun's Gallery
Golden Goose Shoes sheets
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Post subject: Golden Goose Shoes sheets
Location: http://www.goldengoose-ggdb.com/
Unlike Hooters, Duke noted, Tallywackers allows diners to choose their preferred waiter before sitting down to order."We want to hire a wide variety of men," he said. "Everyone has a different type. Another trip by Mnuchin and his wife to Kentucky last month is already under review by the Treasury Golden Goose Department's Inspector General. government plane.
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The Guide additionally contains feedback forms for Departments/Offices to complete and return to the Ethics Office. To open or download the document(s), click on the link(s) provided. The base application is lightweight and doesn't have all the clutter that Golden Goose Sneakers a lot of other email clients have, but you can easily add all of the functionality you need with free addons and extras available through the Thunderbird web site. With its colorful user interface, animations, and wide variety of email backgrounds, IncrediMail can be a lot of fun.
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This dress is by far one of the most beautiful, lightweight, and floaty pieces I've ever worn. Cynthia Rowley sent me a couple of pieces to play around with and this off-the-shoulder, silk number was perfect to bring with me to Hawaii last month. I wore it to grab coffee with Julie while in town, but it could easily double as an elegant dinner look or even for cocktails overlooking the sea.
We shot at the Laylow, Autograph Collection while in town and decided it's absolutely photogenic and we need to book at stay there next time.
Photography by my talented friend, Julie.
I am so ready for Summer. Getting a taste of it in Hawaii makes me long for it that much more in San Francisco when I know it won't be officially be here for another few months. Generally we get our warmer climate in September or October, but since the rest of the US is getting their Summertime I'm itching for sweet little dresses, open toed sandals and little straw bags to run around town in.
Pairing my denim with an off-the-shoulder linen top, pale silk slides, a round bag from Bali that I begged my sister to bring back for me, and hat makes getting in that Summertime ease spirit that much sweeter.
A few weeks ago we made it down to Hawaii for our beautiful friends, Julie and Chong's, wedding. We used to stay with friends or in not-so-nice budget hotels since we'd go a lot in college, but we decided to splurge a bit this time around and stay somewhere a bit more luxe. The first place we stayed at was the Shoreline Hotel Waikiki. After a super short flight but a ridiculously long ordeal to get our rental car arriving at the Shoreline was a sweet relief with its refreshing flavored water waiting at the ready, superbly accommodating staff and a picture perfect tropical mid-century vibe in the lobby. Once we checked into our penthouse accommodations, we were greeted with a spacious room and double balconies facing the gorgeous Waikiki city skyline. The double balconies weren't the only surprise; the staff even left us a basket of Hawaiian sweets to nibble on. With the city facing us and the soft breeze of waves in the distance, we knew this was going to be the perfect getaway.
The Shoreline was the best location for us to start our stay since it was so close to restaurants and boutiques just down the street and were within blocks of walking to the beautiful waters of Waikiki beach! I couldn't get enough; I wanted to spend all my time there. Thankfully the Shoreline provided towels and even cute little beach bags to pack a picnic and travel in style while there.
Big thanks to the Shoreline Hotel Waikiki for the accommodating us so graciously! If you are ever in the Waikiki and need a place to stay near the beach, I'd highly recommend the Shoreline.
Not sure when it started but my affection for striped articles of clothing must have begun years ago when I realized the French looked très chic in stripes and just I had to try this trend out. In the beginning, it was more striped top and beret then Jane Birkin circa 1960, but it's evolved, thank God. Since then it's been one whirlwind of striped look, after look, after look, I just couldn't get enough striped tops — striped top with a blazer, striped top with a denim jacket, you literally could wear a striped top with anything, and I sure did.
I think what I ultimately loved about striped clothing is that they made me happy, and like my best looking self (reason #8978 why I keep buying them). So naturally, when Banana Republic sent me these striped pants to try out I was elated! Even just pulling these babies out of the bag sent me all sorts of good vibes— the color is just incredible, it's part orange neon, part navy, and partial skinny white stripes. My mom and husband dubbed them circus pants but they've seen me wear all the trends and this one is pretty chic, I think. I decided to pair my pants with another striped top (also from Banana ironically enough) and completed the look with a long linen duster coat and my favorite heels — boss babe approved! When I walked home at the end of the day, I switched out my heels for casual white sneakers, untucked my shirt, and instantly the outfit was elevated to a chic off-duty version of the outfit above.


Since its first publication in 1978 by Ekman & Friesen, the FACS manual has been The Facial Action Coding System (FACS) Manual is a detailed, technical. Facial Action Coding System Учебное руководство. FACS to taxonomize every conceivable human facial expression. 1 The FACS Manual. Как скачать; Facial Action Coding System (FACS) (2002) CCD Название: Facial Action Coding System Автор (издатель): Paul Ekman.
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Of the many obstacles faced by people who stutter, perhaps the most devastating is discrimination in employment and educational opportunities. As an NSP chapter leader and as current chair of the NSP's Advocacy Committee, I have heard from stutterers who try to hide their stuttering on the job for fear of being fired, who suffer harassment or unfavorable evaluations by intolerant supervisors, and who have been denied promotions to supervisory positions or jobs that involve speaking or dealing with the public.
I personally felt the sting of employment discrimination early in my legal career, when I was openly rejected by firms because of my stuttering, despite my academic qualifications. At the recent NSP convention in Atlanta, I heard similar stories, including that of a speech- language pathology student who was told by his Speech Department that he could not graduate unless he stopped stuttering.
I am convinced that discrimination against stutterers is at least as pervasive as racial or sexual discrimination. In some ways it is even more insidious, because: (1) stutterers are a much smaller minority with less political clout; and (2) many people feel justified in assuming that stuttering is a legitimate job disqualification or a sign of incompetence.
Research has confirmed that persons who stutter are subject to negative stereotypes, which have significantly harmed their employment and promotion opportunities. These stereotypes include the widely accepted impression that stutterers are nervous, shy, quiet, self-conscious, withdrawn, tense, anxious, fearful, reticent, and guarded. For example, one NSP member was denied a promotion by the U.S. Weather Service because his supervisor incorrectly assumed, on the basis of his stuttering, that he lacked the ability "to make rapid fire judgments, think quickly and demonstrate leadership ability."
These negative attitudes have had a significant adverse effect in terms of employment. A 1997 study reported high unemployment for persons who stutter, difficulty in gaining promotions, and a feeling of being discriminated against because of their stuttering. Other studies in 1983 showed that both employers and vocational rehabilitation counselors perceived stuttering to be a vocationally disabling disorder. In a 1991 study, employers reported that, while stuttering does not hinder a person's performance, it does inhibit his or her ability to gain employment or a promotion.
Studies have shown that these negative views of persons who stutter are shared by almost all groups studied - students, teachers, employers, parents, even speech-language pathologists. Even worse, studies show that persons who stutter also believe these stereotypes and tend to behave accordingly. It seems that people who stutter are not only victims of the stereotype, but they themselves may help to perpetuate it. Therefore, we must begin by correcting our own attitudes, if we are to succeed in dispelling the prejudices of others.
Ironically, the negative image of stutterers may be made even worse by our attempts to avoid or to disguise our stuttering. For example, rather than acknowledging a block, we might pretend that we have forgotten the word, can't decide what to say, or don't know the answer to a question. Or we might engage in inappropriate word substitutions or circumlocutions. While we may think we have fooled people by doing this, we really haven't. We have merely confirmed the stereotype that stutterers are hesitant, indecisive, or stupid.
In terms of listener reaction, research has shown that trying to hide our stuttering is actually the worst thing we can do. Studies have shown that listeners have a much more favorable impression of stutters who acknowledge their stuttering than of stutters who do not. Listeners also have a more favorable reaction to actual stuttering blocks, repetitions, and prolongations than to the kind of interjections (um's and ah's, etc.) that we often use when we try to avoid stuttering.
Therefore, if we are to break the negative stereotypes, we must accept and acknowledge our stuttering. We must come "out of the closet" and let employers and others know that stuttering is no stigma and nothing to be ashamed of. For example, we should freely discuss our involvement with the NSP and wear our NSP buttons with pride. If we act as if we are ashamed of ourselves and our stuttering, how can we expect others to treat us with respect?
Despite two decades of publicity about stuttering's possible neurological and hereditary factors, the popular idea that stuttering is caused by "nervousness" continues to persist. Studies indicate that this is due to people's tendency to equate stuttering with their own moments of disfluency - which may have been prompted by nervousness, fear, uncertainty, or emotional conflict. They assume that the stutterer is experiencing similar feelings - only more so. Consequently, they may view stutterers as being nervous, slow, ineffectual, indecisive, or mentally unstable.
We must be ready to disabuse people of these harmful and incorrect assumptions by explaining stuttering in terms that they can understand and relate to. It might be helpful to formulate a brief explanation of stuttering in advance. Ideally, such an explanation should be simple enough to fit into a 15-second sound bite, yet concrete enough for people to conceptualize and remember. Of necessity, it cannot be as complete or sophisticated as speech professionals would like or satisfy all of their competing theories. However, it should offer a credible alternative to displace the shame, stigma, and negative stereotypes associated with stuttering.
While emphasizing that stuttering involves neurological factors, rather than being caused by "nervousness" or emotional problems, we must also be careful not to create the negative impression that stutterers are seriously brain-damaged or otherwise defective. Instead, we should eliminate the shame and stigma of stuttering by emphasizing the stutterer's basic normality.
I personally have had success in explaining stuttering to people by telling them that it is not caused by nervousness, but that it might involve, among other things, "a neurological confusion between two basically normal bodily functions - speech and the Valsalva mechanism." I explain that when I feel that speaking may be difficult, I tend to activate the Valsalva mechanism - which is a bodily mechanism that everybody normally uses to help them to exert effort or to force things out of the body, but which causes a blockage of speech. I might then have them perform a simple exercise which activates the Valsalva mechanism, causing them to personally experience physical blockage in the mouth or larynx - similar to what may happen during a stuttering block. (For a further description, see my article, The Valsalva Mechanism: A Key to Understanding and Controlling Stuttering, or my book, Understanding and Controlling Stuttering, available from the NSP.) While this explanation mentions only one of many possible factors involved in stuttering, it has the advantage of focusing on a normal bodily function that people can personally experience and remember. It also allows for a discussion of stuttering in terms that are free of any possible shame or stigma.
Our right to equal opportunity should not be conditioned upon our fluency. We should have the right to accept our stuttering and to insist that employers judge us solely upon our ability to perform the essential requirements of the job in question.
We should not allow employers to put undue emphasis on "communication skills" as a pretext for discriminating against stuttering. Employers must learn that (except perhaps in the most severe cases) stuttering need not interfere with effective communication! The greatest obstacle to communication comes when we feel compelled to hide our stuttering out of fear of reprisal. For employers to demand fluency as the price of one's job only creates a vicious spiral of stress and anxiety that makes stuttering worse.
When all else fails, we must be ready to use legal remedies to challenge acts of discrimination. A number or state and federal statues now outlaw discrimination against persons with handicaps or disabilities, which may offer protections to persons who stutter. Most recently, the Americans with Disabilities Act of 1990 ("ADA") banned discrimination "against qualified individuals because of a disability, in regard to job application procedures, hiring, advancement, discharge, compensation, job training, and other terms, conditions, and privileges of employment." Each statute has its own specific terms, applicability, and procedures, which must be followed precisely.
Unfortunately, discrimination cases are usually very hard to win, even for experienced attorneys, so they should not be undertaken haphazardly. Although a number of stuttering discrimination cases have been successfully settled before trial, the question of whether stuttering is legally a "disability" entitled to protection is still undecided by the courts. Our greatest fear is that poorly prepared cases will result in unfavorable judicial opinions, which will then be followed by courts in other cases and seriously damage the rights of all persons who stutter.
Therefore, it is important that the NSP's Advocacy Committee be notified of all stuttering discrimination cases, as soon as they are brought or contemplated, so that we can provide whatever advice and assistance might be appropriate.
Because stuttering is such a complex and misunderstood disorder, stuttering discriminations cases must be carefully planned and prepared in order to avoid potential disaster. It would be a tragedy if we allowed the popular prejudices and misconceptions about stuttering to become enshrined as judicial precedent, leaving millions of persons who stutter without legal protection.
A licensed speech-language pathologist and trial lawyer. Author of Understanding & Controlling Stuttering: A Comprehensive New Approach Based on the Valsalva Hypothesis.
Valsalva Control Stuttering Therapy is a new approach to improving fluency by controlling the physiological mechanism that may be causing stuttering blocks. For further information on Valsalva Control Therapy, visit Stuttering Therapy and Counseling at www.stutteringtherapist.com.


The RFI and guidance would allow discrimination under the guise of religion and LGBTQ people, women, religious minorities, and others will face the most harm. As a result, organizations that get funding from HHS could refuse to serve LGBTQ families or single mothers, provide needed reproductive healthcare to victims of sexual assault, or counseling to interfaith couples or couples with a partner who is divorced. HHS could also allow contractors or grantees to refuse to hire a qualified applicant because she uses birth control.
Religious freedom is a fundamental American value. It guarantees us all the right to believe, or not, as we see fit. But it doesn't give anyone the right to use religion as an excuse to harm other people, especially when using taxpayer dollars.