text:
\section{Introduction}

3D object pose estimation and 3D hand pose estimation are two important yet
unsolved vision problems. Traditionally, these two problems have been addressed
separately, yet in many critical applications, we need both capabilities
working together~\cite{tekin:cvpr2019,doosti:cvpr2020,hasson:cvpr2020}. For
example, in robotics, a reliable motion capture for hand manipulation of
objects is crucial for both learning from human
demonstration~\cite{handa:icra2020} and fluent and safe human-robot
interaction~\cite{yang:iros2020}.

State-of-the-art approaches for both 3D object
pose~\cite{tekin:cvpr2018,li:eccv2018,sundermeyer:eccv2018,peng:cvpr2019,wang:cvpr2019a,park:iccv2019,labbe:eccv2020}
and 3D hand pose
estimation~\cite{zimmermann:iccv2017,mueller:cvpr2018,iqbal:eccv2018,boukhayma:cvpr2019,ge:cvpr2019,hasson:cvpr2019,spurr:eccv2020}
rely on deep learning and thus require large datasets with labeled hand or
object poses for training. Many
datasets~\cite{yuan:cvpr2017,zimmermann:iccv2017,hodan:eccv2018,hodan:eccvw2020}
have been introduced in both domains and have facilitated progress on these two
problems in parallel. However, since they were introduced for either task
separately, many of them do not contain interaction of hands and objects, i.e.,
static objects without humans in the scene, or bare hands without interacting
with objects. In the presence of interactions, the challenge of solving the two
tasks together not only doubles but multiplies, due to the motion of
objects and mutual occlusions incurred by the interaction. Networks trained on
either of the datasets will thus not generalize well to interaction scenarios.

Creating a dataset with accurate 3D pose of hands and objects is also
challenging for the same reasons. As a result, prior works have attempted to
capture accurate hand motion either with specialized
gloves~\cite{glauser:siggraph2019}, magnetic
sensors~\cite{yuan:cvpr2017,garcia-hernando:cvpr2018}, or marker-based mocap
systems~\cite{brahmbhatt:eccv2020,taheri:eccv2020}. While they can achieve
unparalleled accuracy, the introduction of hand-attached devices may be
intrusive and thus bias the naturalness of hand motion. It also changes the
appearance of hands and thus may cause issues with generalization.

Due to the challenge of acquiring real 3D poses, there has been an increasing
interest in using synthetic datasets to train pose estimation models. The
success has been notable on object pose estimation. Using 3D scanned object
models and photorealistic rendering, prior
work~\cite{hodan:eccv2018,sundermeyer:eccv2018,tremblay:corl2018,deng:rss2019,hodan:eccvw2020}
has generated synthetic scenes of objects with high fidelity in appearance.
Their models trained only on synthetic data can thus translate to real images.
Nonetheless, synthesizing hand-object interactions remains challenging. One
problem is to synthesize realistic grasp poses for generic
objects~\cite{corona:cvpr2020}. Furthermore, synthesizing natural looking human
motions is still an active research area in graphics.

In this paper, we focus on marker-less data collection of real hand interaction
with objects. We take inspiration from recent work~\cite{hampali:cvpr2020} and
build a multi-camera setup that records interactions synchronously from
multiple views. Compared to the recent work, we instrument the setup with more
cameras and configure them to capture a larger workspace that allows our human
subjects to interact freely with objects. In addition, our pose labeling
process utilizes human annotation rather than automatic labeling. We
crowdsource the annotation so that we can efficiently scale up the data
labeling process. Given the setup, we construct a large-scale dataset that
captures a simple yet ubiquitous task: grasping objects from a table. The
dataset, DexYCB, consists of 582K RGB-D frames over 1,000 sequences of 10
subjects grasping 20 different objects from 8 views (Fig.~\ref{fig:dexycb}).

Our contributions are threefold. First, we introduce a new dataset for
capturing hand grasping of objects. We empirically demonstrate the strength of
our dataset over a related one through cross-dataset evaluation. Second, we
provide in-depth analysis of current approaches thoroughly on three relevant
tasks: 2D object and keypoint detection, 6D object pose estimation, and 3D hand
pose estimation. To the best of our knowledge, our dataset is the first that
allows joint evaluation of these three tasks. Finally, we demonstrate the
importance of joint hand and object pose estimation on a new robotics relevant
task: generating safe robot grasps for human-to-robot object
handover.

\section{Constructing DexYCB}

\subsection{Hardware Setup}

In order to construct the dataset, we built a multi-camera setup for capturing
human hands interacting with objects.
A key design choice was to enable a sizable capture space, where a human
subject can freely interact and perform tasks with multiple objects. Our
multi-camera setup is shown in Fig.~\ref{fig:hardware}. We use 8 RGB-D cameras
(RealSense D415) and mount them such that collectively they can capture a
tabletop workspace with minimal blind spots. The cameras are extrinsically
calibrated and temporally synchronized. For data collection, we stream and
record all 8 views together at 30 fps with both color and depth of resolution
$640\times480$.

\begin{figure}[t]

 \centering
 \includegraphics[width=0.96\linewidth]{setup.png}

 \caption{\small Our setup with 8 RGB-D cameras (red circle).}
 \vspace{-2mm}
 \label{fig:hardware}
\end{figure}

\subsection{Data Collection and Annotation}

Given the setup, we record videos of hands grasping objects. We use 20 objects
from the YCB-Video dataset~\cite{xiang:rss2018}, and record multiple trials
from 10 subjects. For each trial, we select a target object with 2 to
4 other objects and place them on the table. We ask the subject to start from
a relaxed pose, pick up the target object, and hold it in the air.
We also ask some subjects to pretend to hand over the object to someone across from them. We
record for 3 seconds, which is sufficient to contain the full course of action.
For each target object, we repeat the trial 5 times, each time with a random
set of accompanied objects and placement. We ask the subject to perform the
pick-up with the right hand in the first two trials, and with the left hand in
the third and fourth trials. In the fifth trial, we randomize the choice. We
rotate the target among all 20 objects. This gives us 100 trials per subject,
and 1,000 trials in total for all subjects.

To acquire accurate ground-truth 3D pose for hands and objects, our approach
(detailed in Sec.~\ref{sec:solve}) relies on 2D keypoint annotations for hands
and objects in each view. To ensure accuracy, we label the required keypoints in
RGB sequences fully through human annotation. Our annotation tool is based on
VATIC~\cite{vondrick:ijcv2013} for efficient annotation of videos. We set up
annotation tasks on the Amazon Mechanical Turk (MTurk) and label every view in all
the sequences.

For hands, we adopt 21 pre-defined hand joints as our keypoints (3 joints plus
1 tip for each finger and the wrist). We explicitly ask the annotators to label
and track these joints throughout a given video sequence. The annotators are
also asked to mark a keypoint as invisible in a given frame when it is
occluded.

Pre-defining keypoints exhaustively for every object would be laborious
and does not scale as the number of objects increases. Our approach
(Sec.~\ref{sec:solve}) explicitly addresses this issue by allowing user-defined
keypoints. Specifically, given a video sequence in a particular view, we first
ask the annotator to find 2 distinctive landmark points that are easily
identified and trackable on a designated object, and we ask them to label and
track these points throughout the sequence. We explicitly ask the annotators to
find keypoints that are visible most of the time, and mark a
keypoint as invisible whenever it is occluded.

\subsection{Solving 3D Hand and Object Pose}
\label{sec:solve}

To represent 3D hand pose, we use the popular MANO hand
model~\cite{romero:siggraphasia2017}. The model represents a right or left hand
with a deformable triangular mesh of 778 vertices. The mesh is parameterized by
two low-dimensional embeddings $(\theta,\beta)$, where
$\theta\in\mathbb{R}^{51}$ accounts for variations in pose (i.e. articulation)
and $\beta\in\mathbb{R}^{10}$ in shape. We use the version
from~\cite{hasson:cvpr2019}, which implements MANO as a differentiable layer in
PyTorch that maps $(\theta,\beta)$ to the mesh together with the 3D positions
of 21 hand joints defined in the keypoint annotation. We pre-calibrate the hand
shape $\beta$ for each subject and fix it throughout each subject's sequences.

Since our objects from YCB-Video~\cite{xiang:rss2018} also come with texture-mapped 3D mesh models, we use the standard 6D pose
representation~\cite{hodan:eccv2018,hodan:eccvw2020} for 3D object pose. The pose of each
object is represented by a matrix $T\in\mathbb{R}^{3\times4}$ composed of a
3D rotation matrix and a 3D translation vector.

To solve for hand and object pose, we formulate an optimization problem similar
to~\cite{zimmermann:iccv2019,hampali:cvpr2020} by leveraging depth and keypoint
annotations from all views and multi-view geometry. For a given sequence with
$N_{H}$ hands and $N_{O}$ objects, we denote the overall pose at a given time
frame by $P=(P_{H},P_{O})$, where $P_{H}=\{\theta_{h}\}_{h=1}^{N_{H}}$ and
$P_{O}=\{T_{o}\}_{o=1}^{N_{O}}$. We define the pose in world coordinates
where we know the extrinsics of each camera. Then at each time frame, we solve
the pose by minimizing the following energy function:
\begin{equation}
 \label{eq:energy}
 E(P)=E_{\text{depth}}(P)+E_{\text{kpt}}(P)+E_{\text{reg}}(P).
\end{equation}

\vspace{-3mm}
\paragraph{Depth} The depth term $E_{\text{depth}}$ measures how well the
models given poses explain the observed depth data. Let
$\{d_{i}\in\mathbb{R}^{3}\}_{i=1}^{N_{D}}$ be the total point cloud merged from
all views after transforming to the world coordinates, with $N_{D}$ denoting the
number of points. Given a pose parameter, we denote the collection of all hand
and object meshes as
$\mathcal{M}(P)=(\{\mathcal{M}_{h}(\theta_{h})\},\{\mathcal{M}_{o}(T_{o})\})$.
We define the depth term as
\begin{equation}
 E_{\text{depth}}(P)=\frac{1}{N_{D}}\sum_{i=1}^{N_{D}}|\text{SDF}(d_{i},\mathcal{M}(P))|^{2},
\end{equation}
where $\text{SDF}(\cdot)$ calculates the signed distance value of a 3D point
from a triangular mesh in mm. While $E_{\text{depth}}$ is differentiable,
calculating $E_{\text{depth}}$ and also the gradients is computationally
expensive for large point clouds and meshes with a huge number of vertices.
Therefore, we use an efficient point-parallel GPU implementation for it.

\vspace{-3mm}
\paragraph{Keypoint} The keypoint term $E_{\text{kpt}}$ measures the
reprojection error of the keypoints on the models with the annotated keypoints,
and can be decomposed by hand and object:
\begin{equation}
 E_{\text{kpt}}(P)=E_{\text{kpt}}(P_{H})+E_{\text{kpt}}(P_{O}).
\end{equation}

For hands, let $J_{h,j}$ be the 3D position of joint $j$ of hand $h$ in the world
coordinates, $p_{h,j}^{c}$ be the annotation of the same joint in the image
coordinates of view $c$, and $\gamma_{h,j}^{c}$ be its visibility indicator.
The energy term is defined as
\begin{equation}
 \label{eq:hand}
 E_{\text{kpt}}(P_{H})=\frac{1}{\sum\gamma_{h,j}^{c}}\sum_{c=1}^{N_{C}}\sum_{h=1}^{N_{H}}\sum_{j=1}^{N_{J}}\gamma_{h,j}^{c}||\text{proj}^{c}(J_{h,j})-p_{h,j}^{c}||_{2}^{2},
\end{equation}
where $\text{proj}^{c}(\cdot)$ returns the projection of a 3D point onto the
image plane of view $c$, and $N_{C}=8$ and $N_{J}=21$.

For objects, recall that we did not pre-define keypoints for annotation, but
rather asked annotators to select distinctive points to track. Here, we assume an
accurate initial pose is given at the first frame where an object's
keypoint is labeled visible. We then map the selected keypoint to a vertex on the
object's 3D model by back-projecting the keypoint's position onto the object's
visible surface. We fix that mapping afterwards. Let $K_{o,k}^{c}$ be the 3D
position of the selected keypoint $k$ of object $o$ in view $c$ in world
coordinates. Similar to Eq.~\eqref{eq:hand}, with $N_{K}=2$, the energy term is
\begin{equation}
 E_{\text{kpt}}(P_{O})=\frac{1}{\sum\gamma_{o,k}^{c}}\sum_{c=1}^{N_{C}}\sum_{o=1}^{N_{O}}\sum_{k=1}^{N_{K}}\gamma_{o,k}^{c}||\text{proj}^{c}(K_{o,k}^{c})-p_{o,k}^{c}||_{2}^{2}.
\end{equation}
To ensure an accurate initial pose for keypoint mapping, we initialize the pose
in each time frame with the solved pose from the last time frame. We initialize
the pose in the first frame by running PoseCNN~\cite{xiang:rss2018} on each
view and select an accurate pose for each object manually.

\vspace{-3mm}
\paragraph{Regularization} Following
\cite{mueller:siggraph2019,hasson:cvpr2020}, we add an $\ell_{2}$
regularization to the low-dimensional pose embedding of MANO to avoid irregular
articulation of hands:
\begin{equation}
 E_{\text{reg}}(P)=\frac{1}{N_{H}}\sum_{h=1}^{N_{H}}||\theta_{h}||_{2}^{2}.
\end{equation}
To minimize Eq.~\eqref{eq:energy}, we use the Adam optimizer with a learning rate
of 0.01. For each time frame, we initialize the pose $P$ with the solved pose
from the last time frame and run the optimizer for 100 iterations.

\section{Related Datasets}

\subsection{6D Object Pose}

Most datasets address instance-level 6D object pose estimation, where 3D models
are given a priori. The recent BOP
challenge~\cite{hodan:eccv2018,hodan:eccvw2020} has curated a decent line-up of
these datasets which the participants have to evaluate on. Yet objects are
mostly static in these datasets without human interactions. A recent
dataset~\cite{wang:cvpr2019b} was introduced for category-level 6D pose
estimation, but the scenes are also static.

\begin{table*}[t]

 \centering
 \footnotesize
 \setlength{\tabcolsep}{1.30pt}
 \begin{tabular}{l|ccccccccrrrrcrc|c}
  \multirow{3}{*}{dataset} & \multirow{3}{*}{\minitab[c]{visual\\modality}} & \multirow{3}{*}{\minitab[c]{real\\image}} & \multirow{3}{*}{\minitab[c]{marker-\\less}} & hand- & hand- & 3D   & 3D    & \multirow{3}{*}{resolution} & \multirow{3}{*}{\#frames} & \multirow{3}{*}{\#sub} & \multirow{3}{*}{\#obj} & \multirow{3}{*}{\#views} & \multirow{3}{*}{motion} & \multirow{3}{*}{\#seq} & \multirow{3}{*}{\minitab[c]{dynamic\\grasp}} & \multirow{3}{*}{label} \\
                           &                                                &                                           &                                             & hand  & obj   & obj  & hand  &                             &                           &                        &                        &                          &                         &                        &                                              &                        \\
                           &                                                &                                           &                                             & int   & int   & pose & shape &                             &                           &                        &                        &                          &                         &                        &                                              &                        \\
  \hline
  \multirow{2}{*}{BigHand2.2M~\cite{yuan:cvpr2017}} & \multirow{2}{*}{depth} & \multirow{2}{*}{{\color{Green4} \checkmark}} & \multirow{2}{*}{{\color{red}$\times$}} & \multirow{2}{*}{{\color{red}$\times$}} & \multirow{2}{*}{{\color{red}$\times$}} & \multirow{2}{*}{--} & \multirow{2}{*}{{\color{red}$\times$}} & \multirow{2}{*}{640$\times$480} & \multirow{2}{*}{2,200K} & \multirow{2}{*}{10} & \multirow{2}{*}{--} & \multirow{2}{*}{1} & \multirow{2}{*}{{\color{Green4} \checkmark}} & \multirow{2}{*}{--} & \multirow{2}{*}{--} & magnetic \\
                                                    &                        &                              &                            &                            &                            &                     &                            &                                 &                         &                     &                     &                    &                              &                     &                     & sensor   \\
  \hline
  SynthHands~\cite{mueller:iccv2017}            & RGB-D & {\color{red}$\times$}   & --          & {\color{red}$\times$}   & {\color{Green4} \checkmark} & {\color{red}$\times$}   & {\color{red}$\times$}   & 640$\times$480   &   220K & -- &  7 &              5 & {\color{red}$\times$}   & --    & --          & synthetic \\
  Rendered Hand Pose~\cite{zimmermann:iccv2017} & RGB-D & {\color{red}$\times$}   & --          & {\color{red}$\times$}   & {\color{red}$\times$}   & --          & {\color{red}$\times$}   & 320$\times$320   &    44K & 20 & -- &              1 & {\color{red}$\times$}   & --    & --          & synthetic \\
  GANerated Hands~\cite{mueller:cvpr2018}       & RGB   & {\color{red}$\times$}   & --          & {\color{red}$\times$}   & {\color{Green4} \checkmark} & {\color{red}$\times$}   & {\color{red}$\times$}   & 256$\times$256   &   331K & -- & -- &              1 & {\color{red}$\times$}   & --    & --          & synthetic \\
  ObMan~\cite{hasson:cvpr2019}                  & RGB-D & {\color{red}$\times$}   & --          & {\color{red}$\times$}   & {\color{Green4} \checkmark} & {\color{Green4} \checkmark} & {\color{Green4} \checkmark} & 256$\times$256   &   154K & 20 & 3K &              1 & {\color{red}$\times$}   & --    & --          & synthetic \\
  \hline
  \multirow{2}{*}{FPHA~\cite{garcia-hernando:cvpr2018}}   & \multirow{2}{*}{RGB-D} & \multirow{2}{*}{{\color{Green4} \checkmark}} & \multirow{2}{*}{{\color{red}$\times$}} & \multirow{2}{*}{{\color{red}$\times$}}   & \multirow{2}{*}{{\color{Green4} \checkmark}} & \multirow{2}{*}{{\color{Green4} \checkmark}} & \multirow{2}{*}{{\color{red}$\times$}}   & \multirow{2}{*}{1920$\times$1080} & \multirow{2}{*}{105K}   & \multirow{2}{*}{6}  & \multirow{2}{*}{4}  & \multirow{2}{*}{1} & \multirow{2}{*}{{\color{Green4} \checkmark}} & \multirow{2}{*}{1,175} & \multirow{2}{*}{{\color{Green4} \checkmark}} & magnetic  \\
                                                          &                        &                              &                            &                              &                              &                              &                              &                                   &                         &                     &                     &                    &                              &                        &                              & sensor    \\
  \multirow{2}{*}{ContactPose~\cite{brahmbhatt:eccv2020}} & \multirow{2}{*}{RGB-D} & \multirow{2}{*}{{\color{Green4} \checkmark}} & \multirow{2}{*}{{\color{red}$\times$}} & \multirow{2}{*}{{\color{red}$\times$}}   & \multirow{2}{*}{{\color{Green4} \checkmark}} & \multirow{2}{*}{{\color{Green4} \checkmark}} & \multirow{2}{*}{{\color{Green4} \checkmark}} & \multirow{2}{*}{960$\times$540}   & \multirow{2}{*}{2,991K} & \multirow{2}{*}{50} & \multirow{2}{*}{25} & \multirow{2}{*}{3} & \multirow{2}{*}{{\color{Green4} \checkmark}} & \multirow{2}{*}{2,303} & \multirow{2}{*}{{\color{red}$\times$}}   & mocap     \\
                                                          &                        &                              &                            &                              &                              &                              &                              &                                   &                         &                     &                     &                    &                              &                        &                              & + thermal \\
  GRAB~\cite{taheri:eccv2020}                             & --                     & --                           & {\color{red}$\times$}                  & {\color{red}$\times$}                    & {\color{Green4} \checkmark}                  & {\color{Green4} \checkmark}                  & {\color{Green4} \checkmark}                  & --                                &                 1 ,624K &                  10 &                  51 &                 -- & {\color{Green4} \checkmark}                  &                  1,335 & {\color{Green4} \checkmark}                  & mocap     \\
  \hline
  Mueller et al.~\cite{mueller:siggraph2019}    & depth & {\color{red}$\times$}   & --          & {\color{Green4} \checkmark} & {\color{red}$\times$}   & --          & {\color{red}$\times$}   & 640$\times$480   &    80K &  5 & -- &              4 & {\color{Green4} \checkmark} &    11 & --          & synthetic \\
  InterHand2.6M~\cite{moon:eccv2020}            & RGB   & {\color{Green4} \checkmark} & {\color{Green4} \checkmark} & {\color{Green4} \checkmark} & {\color{red}$\times$}   & --          & {\color{Green4} \checkmark} & 512$\times$334   & 2,590K & 27 & -- & \textgreater80 & {\color{Green4} \checkmark} &    -- & --          & semi-auto \\
  \hline
  Dexter+Object~\cite{sridhar:eccv2016}         & RGB-D & {\color{Green4} \checkmark} & {\color{Green4} \checkmark} & {\color{red}$\times$}   & {\color{Green4} \checkmark} & {\color{Green4} \checkmark} & {\color{red}$\times$}   & 640$\times$480   &     3K &  2 &  2 &              1 & {\color{Green4} \checkmark} &     6 & {\color{Green4} \checkmark} & manual    \\
  Simon et al.~\cite{simon:cvpr2017}            & RGB   & {\color{Green4} \checkmark} & {\color{Green4} \checkmark} & {\color{Green4} \checkmark} & {\color{Green4} \checkmark} & {\color{red}$\times$}   & {\color{red}$\times$}   & 1920$\times$1080 &    15K & -- & -- &             31 & {\color{Green4} \checkmark} &    -- & {\color{Green4} \checkmark} & automatic \\
  EgoDexter~\cite{mueller:iccv2017}             & RGB-D & {\color{Green4} \checkmark} & {\color{Green4} \checkmark} & {\color{red}$\times$}   & {\color{Green4} \checkmark} & {\color{red}$\times$}   & {\color{red}$\times$}   & 640$\times$480   &     3K &  4 & -- &              1 & {\color{Green4} \checkmark} &     4 & {\color{Green4} \checkmark} & manual    \\
  FreiHAND~\cite{zimmermann:iccv2019}           & RGB   & {\color{Green4} \checkmark} & {\color{Green4} \checkmark} & {\color{red}$\times$}   & {\color{Green4} \checkmark} & {\color{red}$\times$}   & {\color{Green4} \checkmark} & 224$\times$224   &    37K & 32 & 27 &              8 & {\color{red}$\times$}   &    -- & --          & semi-auto \\
  HO-3D~\cite{hampali:cvpr2020}                 & RGB-D & {\color{Green4} \checkmark} & {\color{Green4} \checkmark} & {\color{red}$\times$}   & {\color{Green4} \checkmark} & {\color{Green4} \checkmark} & {\color{Green4} \checkmark} & 640$\times$480   &    78K & 10 & 10 &           1--5 & {\color{Green4} \checkmark} &    27 & {\color{Green4} \checkmark} & automatic \\
  DexYCB (ours)                                 & RGB-D & {\color{Green4} \checkmark} & {\color{Green4} \checkmark} & {\color{red}$\times$}   & {\color{Green4} \checkmark} & {\color{Green4} \checkmark} & {\color{Green4} \checkmark} & 640$\times$480   &   582K & 10 & 20 &              8 & {\color{Green4} \checkmark} & 1,000 & {\color{Green4} \checkmark} & manual
 \end{tabular}
 \vspace{-2mm}
 \caption{\small Comparison of DexYCB with existing 3D hand pose datasets.}
 \vspace{-3mm}
 \label{tab:dataset}
\end{table*}

\subsection{3D Hand Pose}

We present a summary of related 3D hand pose datasets in
Tab.~\ref{tab:dataset}. Some address pose estimation with depth only, e.g.,
BigHand2.2M~\cite{yuan:cvpr2017}. These datasets can be large but lack color
images and capture only bare hands without interactions. Some others address
hand-hand interactions, e.g., Mueller et al.~\cite{mueller:siggraph2019} and
InterHand2.6M~\cite{moon:eccv2020}. While not focused on interaction with
objects, their datasets address another challenging scenario and is orthogonal
to our work. Below we review datasets with hand-object interactions.

\vspace{-3mm}
\paragraph{Synthetic} Synthetic data has been increasingly used for hand pose
estimation~\cite{mueller:iccv2017,zimmermann:iccv2017,mueller:cvpr2018,hasson:cvpr2019}.
A common downside is the gap to real images on appearance. To bridge this gap,
GANerated Hands~\cite{mueller:cvpr2018} was introduced by translating synthetic
images to real via GANs. Nonetheless, other challenges remain. One is to
synthesize realistic grasp pose for objects. ObMan~\cite{hasson:cvpr2019} was
introduced to address this challenge using heuristic metrics. Yet besides pose,
it is also challenging to synthesize realistic motion. Consequently, these
datasets only offer static images but not videos. Our dataset captures real
videos with real grasp pose and motion. Furthermore, our motion data from real
can help bootstrapping synthetic data generation.

\vspace{-3mm}
\paragraph{Marker-based} FPHA~\cite{garcia-hernando:cvpr2018} captured hand
interaction with objects in first person view by attaching magnetic sensors to
the hands. This offers a flexible and portable solution, but the attached
sensors may hinder natural hand motion and also bias the hand appearance.
ContactPose~\cite{brahmbhatt:eccv2020} captured grasp poses by tracking objects
with mocap markers and recovering hand pose from thermal imagery of the objects
surface. While the dataset is large in scale, the thermal based approach can
only capturing rigid grasp poses but not motions. GRAB~\cite{taheri:eccv2020}
captured full body motion together with hand-object interactions using
marker-based mocap. It provides high-fidelity capture of interactions but does
not come with any visual modalities. Our dataset is marker-less, captures
dynamic grasp motions, and provides RGB-D sequences in multiple views.

\vspace{-3mm}
\paragraph{Marker-less} Our DexYCB dataset falls in this category. The
challenge is to acquire accurate 3D pose. Some datasets like
Dexter+Object~\cite{sridhar:eccv2016} and EgoDexter~\cite{mueller:cvpr2018}
rely on manual annotation and are thus limited in scale. To acquire 3D pose at
scale others rely on automatic or semi-automatic
approaches~\cite{simon:cvpr2017,zimmermann:iccv2019}. While these datasets
capture hand-object interactions, they do not offer 3D pose for objects.

Most similar to ours is the recent HO-3D dataset~\cite{hampali:cvpr2020}. HO-3D
also captures hands interacting with objects from multiple views and provides
both 3D hand and object pose. Nonetheless, the two datasets differ both
quantitatively and qualitatively. Quantitatively (Tab.~\ref{tab:dataset}),
DexYCB captures interactions with more objects (20 versus 10), from more views
(8 versus 1 to 5), and is one order of magnitude larger in terms of number of
frames (582K versus 78K)~\footnote{We count the number of frames over all
views.} and number of sequences (1,000 versus 27).~\footnote{We count the same
motion across different views as one sequence.} Qualitatively
(Fig.~\ref{fig:ho3d}), we highlight three differences. First, DexYCB captures
full grasping processes (i.e. from hand approaching, opening fingers, contact,
to holding the object stably) in short segments, whereas HO-3D captures longer
sequences with object in hand most of the time. Among the 27 sequences from
HO-3D, we found 17 with hand always rigidly attached to the object, only
rotating the object from the wrist with no finger articulation. Second, DexYCB
captures a full tabletop workspace with 3D pose of all the objects on the table,
whereas in HO-3D one object is held close to the camera each time and only the
held object is labeled with pose. Finally, regarding annotation, DexYCB
leverages keypoints fully labeled by humans through crowdsourcing, while HO-3D
relies on a fully automatic labeling pipeline.

\begin{figure}[t]

 \centering
 \includegraphics[width=0.32\linewidth]{related-MC1-0070.png}~
 \includegraphics[width=0.32\linewidth]{related-MC1-0090.png}~
 \includegraphics[width=0.32\linewidth]{related-MC1-0110.png}
 \\ \vspace{1mm}
 \includegraphics[width=0.32\linewidth]{related-20200820_135545-color_000005_v2.jpg}~
 \includegraphics[width=0.32\linewidth]{related-20200820_135545-color_000024_v2.jpg}~
 \includegraphics[width=0.32\linewidth]{related-20200820_135545-color_000042_v2.jpg}

 \caption{\small Qualitative comparison of HO-3D~\cite{hampali:cvpr2020} (top)
and DexYCB (bottom). DexYCB captures full grasping processes in a tabletop
workspace with 3D pose of all the on table objects.}
 \vspace{-4mm}
 \label{fig:ho3d}
\end{figure}

\section{Cross-Dataset Evaluation}
\label{sec:cross}

To further assess the merit of our DexYCB dataset, we perform cross-dataset
evaluation following prior
work~\cite{zimmermann:iccv2019,shan:cvpr2020,yu:cvpr2020}. We focus
specifically on HO-3D~\cite{hampali:cvpr2020} due to its relevance, and
evaluate generalizability between HO-3D and DexYCB on single-image 3D hand pose
estimation. For DexYCB, we generate a train/val/test split following our
benchmark setup (``S0'' in Sec.~\ref{sec:eval}). For HO-3D, we select 6 out of
its 55 training sequences for test. We use Spurr et al.~\cite{spurr:eccv2020}
(winner of the HAND 2019 Challenge) as our method, and train the model
separately on three training sets: the training set of HO-3D, the training set
of DexYCB, and the combined training set from both datasets. Finally, we
evaluate the three trained models separately on the test set of HO-3D and
DexYCB.

Tab.~\ref{tab:cross} shows the results in mean per joint position error (MPJPE)
reported on two different alignment methods (details in Sec.~\ref{sec:3d}). We
experiment with two different backbones: ResNet50 and
HRNet32~\cite{sun:cvpr2019}. Unsurprisingly, when training on a single dataset,
the model generalizes better to the respective test set. The error increases
when tested on the other dataset. When we evaluate the DexYCB trained model on
HO-3D, we observe a consistent increase from 1.4$\times$ to 1.9$\times$ (e.g.,
for ResNet50, from 18.05 to 31.76 mm on root-relative). However, when we
evaluate the HO-3D trained model on DexYCB, we observe a consistent yet more
significant increase, from 3.4$\times$ to 3.7$\times$ (e.g., for ResNet50, from
12.97 to 48.30 mm on root-relative). This suggests that models trained on
DexYCB generalize better than on HO-3D. Furthermore, when we train on the
combined training set and evaluate on HO-3D, we can further reduce the error
from only training on HO-3D (e.g., from 18.05 to 15.79 mm). However, when tested
on DexYCB, the error rather rises compared to training only on DexYCB (e.g.,
from 12.97 to 13.36 mm). We conclude that DexYCB complements HO-3D better than
vice versa.

\begin{table}[t]

 \centering
 \small

 \begin{tabular}{l|cc|c}
  \multirow{2}{*}{\backslashbox{test}{train}} & \multirow{2}{*}{HO-3D~\cite{hampali:cvpr2020}} & \multirow{2}{*}{DexYCB} & HO-3D~\cite{hampali:cvpr2020} \\
                                              &                                                &                         & + DexYCB                      \\
  \hline
  HO-3D                                       & 18.05 / 10.66 & 31.76 / 15.23  & 15.79 / 9.51 \\
  DexYCB                                      & 48.30 / 24.23 & 12.97 / ~~7.18 & 13.36 / 7.27 \\
  \hline
  \hline
  HO-3D                                       & 17.46 / 10.44 & 33.11 / 15.51  & 15.89 / 9.00 \\
  DexYCB                                      & 46.38 / 23.94 & 12.39 / ~~6.79 & 12.48 / 6.87
 \end{tabular}
 \vspace{-2mm}
 \caption{\small Cross-dataset evaluation with HO-3D~\cite{hampali:cvpr2020} on 3D hand pose estimation. Results are in MPJPE (mm) (root-relative / Procrustes aligned). Top: \cite{spurr:eccv2020} + ResNet50. Bottom: \cite{spurr:eccv2020} + HRNet32.}

 \label{tab:cross}
\end{table}

\section{Benchmarking Representative Approaches}
\label{sec:benchmark}

We benchmark three tasks on our DexYCB dataset: 2D object and keypoint
detection, 6D object pose estimation, and 3D hand pose estimation. For each
task we select representative approaches and analyze their performace.

\subsection{Evaluation Setup}
\label{sec:eval}

To evaluate different scenarios, we generate train/val/test splits in four
different ways (referred to as ``setup''):

\begin{myitemize}
 \item[$\bullet$] \textbf{S0 (default).} The train split contains all 10
subjects, all 8 camera views, and all 20 grasped objects. Only the sequences
are not shared with the val/test split.
 \item[$\bullet$] \textbf{S1 (unseen subjects).} The dataset is split by
subjects (train/val/test: 7/1/2).
 \item[$\bullet$] \textbf{S2 (unseen views).} The dataset is split by camera
views (train/val/test: 6/1/1).
 \item[$\bullet$] \textbf{S3 (unseen grasping).} The dataset is split by
grasped objects (train/val/test: 15/2/3). Objects being grasped in the test
split are never being grasped in the train/val split, but may appear static on
the table. This way the training set still contain examples of each object.
\end{myitemize}

\subsection{2D Object and Keypoint Detection}
\label{sec:2d}

We evaluate object and keypoint detection using the COCO evaluation
protocol~\cite{lin:eccv2014}. For object detection, we consider 20 object
classes and 1 hand class. We collect ground truths by rendering a segmentation
mask for each instance in each camera view. For keypoints, we consider 21 hand
joints and collect the ground truths by reprojecting each 3D joint to each
camera image.

We benchmark two representative approaches: Mask R-CNN
(Detectron2)~\cite{he:iccv2017,wu2019detectron2} and
SOLOv2~\cite{wang:neurips2020}. Mask R-CNN has been the de facto for object and
keypoint detection and SOLOv2 is a state-of-the-art on COCO instance
segmentation. For both we use a ResNet50-FPN backbone pre-trained on ImageNet
and finetune on DexYCB.

\begin{table*}[t]

 \centering
 \footnotesize
 \setlength{\tabcolsep}{2.06pt}
 \begin{tabular}{L{2.54cm}|C{0.77cm}C{0.77cm}|C{0.77cm}C{0.77cm}|C{0.77cm}C{0.77cm}|C{0.77cm}C{0.77cm}|C{0.77cm}C{0.77cm}|C{0.77cm}C{0.77cm}|C{0.77cm}C{0.77cm}|C{0.77cm}C{0.77cm}}
                           & \multicolumn{4}{c|}{S0 (default)} & \multicolumn{4}{c|}{S1 (unseen subjects)} & \multicolumn{4}{c|}{S2 (unseen views)} & \multicolumn{4}{c}{S3 (unseen grasping)} \\
                           & \multicolumn{2}{c|}{Mask R-CNN} & \multicolumn{2}{c|}{SOLOv2} & \multicolumn{2}{c|}{Mask R-CNN} & \multicolumn{2}{c|}{SOLOv2} & \multicolumn{2}{c|}{Mask R-CNN} & \multicolumn{2}{c|}{SOLOv2} & \multicolumn{2}{c|}{Mask R-CNN} & \multicolumn{2}{c}{SOLOv2} \\
                           & bbox & segm & bbox & segm & bbox & segm & bbox & segm & bbox & segm & bbox & segm & bbox & segm & bbox & segm \\
  \hline
  002\_master\_chef\_can   & 83.87          & 82.68          & \textbf{84.90} & \textbf{85.20} & 82.41          & 80.69          & \textbf{82.74} & \textbf{81.67} & \textbf{85.82} & \textbf{84.44} & 83.52          & 83.39          & 83.96          & 83.72          & \textbf{84.53} & \textbf{85.44} \\
  003\_cracker\_box        & 85.85          & 83.72          & \textbf{89.08} & \textbf{88.07} & 82.12          & 80.18          & \textbf{84.26} & \textbf{84.39} & 88.74          & 87.60          & \textbf{89.61} & \textbf{87.85} & \textbf{85.51} & 83.68          & 79.75          & \textbf{88.00} \\
  004\_sugar\_box          & \textbf{81.30} & 77.71          & 80.45          & \textbf{79.26} & \textbf{78.35} & 74.75          & 77.17          & \textbf{76.66} & \textbf{83.72} & \textbf{79.53} & 81.91          & 79.25          & \textbf{78.06} & 75.68          & 74.87          & \textbf{77.40} \\
  005\_tomato\_soup\_can   & \textbf{76.03} & 74.75          & 75.70          & \textbf{75.33} & \textbf{73.65} & 71.33          & 72.66          & \textbf{71.35} & \textbf{77.66} & \textbf{76.70} & 75.25          & 73.82          & \textbf{76.70} & 76.29          & 76.50          & \textbf{76.48} \\
  006\_mustard\_bottle     & \textbf{81.56} & 79.40          & 79.94          & \textbf{81.87} & \textbf{78.76} & 75.41          & 77.76          & \textbf{75.92} & \textbf{81.12} & \textbf{79.06} & 79.87          & 78.29          & 81.74          & 82.37          & \textbf{82.24} & \textbf{84.45} \\
  007\_tuna\_fish\_can     & \textbf{68.08} & 67.14          & 67.76          & \textbf{67.34} & \textbf{68.99} & \textbf{67.76} & 68.07          & 67.59          & \textbf{73.37} & \textbf{72.06} & 71.72          & 70.68          & \textbf{75.76} & \textbf{77.70} & \textbf{75.76} & 77.61          \\
  008\_pudding\_box        & 73.60          & 70.39          & \textbf{74.05} & \textbf{72.58} & 69.77          & 68.04          & \textbf{69.78} & \textbf{68.20} & 78.88          & \textbf{76.74} & \textbf{79.01} & 76.58          & \textbf{71.32} & \textbf{71.43} & 65.11          & 70.94          \\
  009\_gelatin\_box        & \textbf{69.51} & \textbf{68.43} & 68.64          & 68.09          & \textbf{67.37} & \textbf{64.99} & 66.16          & 63.14          & 72.87          & \textbf{71.45} & \textbf{73.34} & 69.36          & \textbf{61.19} & 58.41          & 60.98          & \textbf{59.49} \\
  010\_potted\_meat\_can   & 75.63          & 72.66          & \textbf{77.28} & \textbf{75.80} & \textbf{73.86} & \textbf{71.95} & 70.87          & 71.76          & 78.96          & \textbf{78.35} & \textbf{79.63} & 76.84          & 77.48          & 78.03          & \textbf{78.40} & \textbf{79.24} \\
  011\_banana              & \textbf{70.53} & 63.43          & 69.67          & \textbf{65.49} & \textbf{68.07} & 60.50          & 67.31          & \textbf{60.57} & \textbf{72.46} & 58.18          & 70.80          & \textbf{70.68} & 71.67          & 67.54          & \textbf{73.25} & \textbf{70.28} \\
  019\_pitcher\_base       & 87.17          & 84.67          & \textbf{89.95} & \textbf{88.98} & 86.21          & 82.00          & \textbf{90.07} & \textbf{86.38} & \textbf{90.47} & 86.67          & 86.88          & \textbf{87.02} & 85.62          & 83.69          & \textbf{90.22} & \textbf{89.91} \\
  021\_bleach\_cleanser    & \textbf{80.98} & 77.52          & 75.52          & \textbf{79.71} & \textbf{79.59} & 76.68          & 78.77          & \textbf{78.52} & \textbf{84.75} & 81.71          & 82.79          & \textbf{81.75} & \textbf{77.36} & 71.54          & 77.01          & \textbf{75.54} \\
  024\_bowl                & \textbf{80.62} & 78.12          & 75.79          & \textbf{80.30} & \textbf{78.75} & 76.42          & 78.06          & \textbf{77.39} & \textbf{83.25} & \textbf{80.12} & 81.54          & 79.72          & 81.12          & 79.69          & \textbf{81.91} & \textbf{82.99} \\
  025\_mug                 & 76.01          & 71.95          & \textbf{76.35} & \textbf{74.35} & \textbf{74.64} & 69.64          & 72.72          & \textbf{69.93} & \textbf{80.14} & \textbf{75.99} & 78.24          & 74.13          & 75.51          & 73.19          & \textbf{76.78} & \textbf{75.86} \\
  035\_power\_drill        & 81.83          & 73.80          & \textbf{82.85} & \textbf{77.20} & 76.89          & 69.02          & \textbf{77.90} & \textbf{71.50} & \textbf{82.57} & \textbf{74.66} & 82.42          & 74.09          & 83.67          & 77.62          & \textbf{84.06} & \textbf{80.77} \\
  036\_wood\_block         & 83.75          & 81.41          & \textbf{85.72} & \textbf{85.59} & \textbf{80.45} & 78.15          & 79.45          & \textbf{79.32} & \textbf{83.78} & \textbf{83.40} & 41.24          & 64.85          & 77.52          & 73.84          & \textbf{78.01} & \textbf{77.78} \\
  037\_scissors            & \textbf{64.07} & 29.36          & 59.00          & \textbf{32.63} & \textbf{55.05} & 20.68          & 50.85          & \textbf{26.51} & \textbf{63.16} & \textbf{17.93} & 38.00          & 16.93          & \textbf{58.01} & 24.35          & 57.92          & \textbf{31.11} \\
  040\_large\_marker       & \textbf{52.69} & 42.42          & 50.95          & \textbf{42.46} & \textbf{50.14} & \textbf{38.46} & 44.94          & 36.46          & \textbf{53.98} & \textbf{36.47} & 50.78          & 33.58          & \textbf{55.35} & 41.23          & 53.66          & \textbf{45.22} \\
  052\_extra\_large\_clamp & \textbf{73.41} & 54.03          & 71.71          & \textbf{55.38} & \textbf{68.12} & 51.26          & 65.26          & \textbf{52.33} & \textbf{75.61} & 52.77          & 61.14          & \textbf{53.59} & \textbf{75.24} & 57.28          & 72.52          & \textbf{58.87} \\
  061\_foam\_brick         & \textbf{72.68} & 72.85          & 72.54          & \textbf{72.93} & \textbf{68.34} & \textbf{66.62} & 67.98          & 65.72          & \textbf{75.24} & \textbf{74.10} & 73.01          & 71.05          & 71.36          & 71.32          & \textbf{71.50} & \textbf{71.59} \\
  hand                     & \textbf{71.85} & \textbf{54.83} & 66.41          & 54.27          & \textbf{64.88} & \textbf{46.86} & 58.33          & 46.66          & \textbf{70.23} & \textbf{54.70} & 59.28          & 52.08          & \textbf{71.23} & \textbf{54.89} & 66.33          & 53.51          \\
  \hline
  mAP                      & \textbf{75.76} & 69.58          & 75.13          & \textbf{71.56} & \textbf{72.69} & 66.26          & 71.48          & \textbf{67.24} & \textbf{77.94} & \textbf{70.60} & 72.37          & 68.66          & \textbf{75.02} & 69.69          & 74.35          & \textbf{72.05}
 \end{tabular}
 \vspace{-2mm}
 \caption{\small 2D object detection results in AP (\%) on the four setups. We
compare Mask R-CNN (Detectron2)~\cite{he:iccv2017,wu2019detectron2} with
SOLOv2~\cite{wang:neurips2020} on both bounding box (bbox) and segmentation
(segm).}
 \vspace{-2mm}
 \label{tab:2d:object}
\end{table*}

\begin{table}[t]

 \centering
 \footnotesize

 \begin{tabular}{l|C{1.40cm}C{1.40cm}C{1.40cm}C{1.40cm}}
       & S0    & S1    & S2    & S3    \\
  \hline
  hand & 36.42 & 26.85 & 32.90 & 35.18
 \end{tabular}
 \vspace{-2mm}
 \caption{\small 2D keypoint detection results in AP (\%) with Mask R-CNN
(Detectron2)~\cite{he:iccv2017,wu2019detectron2}.}
 \vspace{-2mm}
 \label{tab:2d:keypoint}
\end{table}

Tab.~\ref{tab:2d:object} shows results for object detection in average
precision (AP). First, Mask R-CNN and SOLOv2 perform similarly in mean average
precision (mAP). Mask R-CNN has a slight edge on bounding box (e.g., 75.76
versus 75.13 mAP on S0) while SOLOv2 has a slight edge on segmentation (e.g.,
71.56 versus 69.58 mAP on S0). This is because Mask R-CNN predicts bounding
boxes first and uses them to generate segmentations. Therefore the error can
accumulate for segmentation. SOLOv2 in contrast directly predicts segmentations
and uses it to generate bounding boxes. Second, the AP for hand is lower than
the AP for objects (e.g., for Mask R-CNN, 71.85 for hand versus 75.76 mAP on S0
bbox). This suggests that hands are more difficult to detect than objects,
possibly due to its larger variability in position. Finally, performance varies
across setups. For example, mAP in S1 is lower than in S0 (e.g., for Mask R-CNN,
72.69 versus 75.76 on bbox). This can be attributed to the challenge introduced
by unseen subjects. Tab.~\ref{tab:2d:keypoint} shows the AP for keypoint
detection with Mask R-CNN. We observe a similar trend on the ordering of AP
over different setups.

\subsection{6D Object Pose Estimation}
\label{sec:6d}

We evaluate single-view 6D pose estimation following the BOP challenge
protocol~\cite{hodan:eccv2018,hodan:eccvw2020}. The task asks for a 6D pose
estimate for each object instance given the number of objects and instances in
each image. The evaluation computes recall using three different pose-error
functions (VSD, MSSD, MSPD), and the final score is the average of the three
recall values (AR).

We first analyze the challenge brought by hand grasping by comparing static and
grasped objects. We use PoseCNN (RGB)~\cite{xiang:rss2018} as a baseline and
retrain the model on DexYCB. Tab.~\ref{tab:6d:grasp} shows the AR on S0
evaluated separately on the full test set, static objects only, and grasped
objects only. Without surprise the AR drops significantly when only considering
the grasped objects. This recapitulates the increasing challenge of object pose
estimation under hand interactions. To better focus on this regime, we evaluate
only on the grasped objects in the remaining experiments.

\begin{table}[t]

 \centering
 \footnotesize

 \begin{tabular}{C{1.40cm}|C{1.40cm}C{1.40cm}}
  all   & static & grasped \\
  \hline
  52.68 & 56.53  & 41.65
 \end{tabular}
 \vspace{-2mm}
 \caption{\small PoseCNN (RGB)~\cite{xiang:rss2018} results in AR (\%) on S0
(default).}
 \vspace{-2mm}
 \label{tab:6d:grasp}
\end{table}

\begin{table*}[t]

 \centering
 \footnotesize
 \setlength{\tabcolsep}{4.9pt}
 \begin{tabular}{l|cccc||cccccccc}
                           & \multirow{2}{*}{S0} & \multirow{2}{*}{S1} & \multirow{2}{*}{S2} & \multirow{2}{*}{S3} & \multicolumn{2}{c}{PoseCNN~\cite{xiang:rss2018}} & \multicolumn{2}{c}{DeepIM~\cite{li:eccv2018}} & DOPE~\cite{tremblay:corl2018} & \multicolumn{2}{c}{PoseRBPF~\cite{deng:rss2019}} & CosyPose~\cite{labbe:eccv2020} \\
                           & & & & & RGB & + depth ref & RGB & RGB-D & RGB & RGB & RGB-D & RGB \\
  \hline
  002\_master\_chef\_can   & 47.47 & 44.53 & 51.36 & --    & 44.53 & 48.24 & 60.74 & 68.40          & 19.82 & 35.19 & 58.04          & \textbf{77.21} \\
  003\_cracker\_box        & 61.04 & 57.46 & 60.65 & --    & 57.46 & 62.62 & 79.43 & 84.56          & 53.50 & 43.62 & 67.37          & \textbf{88.39} \\
  004\_sugar\_box          & 45.11 & 36.00 & 51.73 & --    & 36.00 & 42.45 & 51.29 & 59.34          & 32.06 & 30.55 & 56.55          & \textbf{69.61} \\
  005\_tomato\_soup\_can   & 36.68 & 34.88 & 45.44 & --    & 34.88 & 42.99 & 46.87 & \textbf{55.46} & 20.52 & 23.76 & 40.03          & 52.76          \\
  006\_mustard\_bottle     & 52.56 & 42.89 & 51.96 & --    & 42.89 & 48.98 & 55.52 & 63.31          & 23.66 & 28.98 & 54.93          & \textbf{67.11} \\
  007\_tuna\_fish\_can     & 32.70 & 29.58 & 31.96 & --    & 29.58 & 36.17 & 39.18 & 46.98          &~~5.86 & 20.13 & 36.77          & \textbf{49.00} \\
  008\_pudding\_box        & 44.24 & 42.60 & 50.81 & --    & 42.60 & 51.26 & 58.23 & 67.18          & 14.48 & 27.24 & 47.95          & \textbf{70.22} \\
  009\_gelatin\_box        & 46.62 & 34.39 & 44.38 & 33.07 & 34.39 & 41.71 & 47.89 & 55.54          & 21.81 & 31.27 & 46.14          & \textbf{57.63} \\
  010\_potted\_meat\_can   & 37.41 & 42.10 & 45.08 & --    & 42.10 & 51.64 & 60.19 & \textbf{69.40} & 14.15 & 31.17 & 46.43          & 65.37          \\
  011\_banana              & 38.33 & 36.71 & 44.42 & --    & 36.71 & 40.21 & 41.82 & \textbf{48.63} & 16.15 & 21.39 & 39.15          & 35.48          \\
  019\_pitcher\_base       & 53.49 & 46.03 & 53.51 & --    & 46.03 & 49.62 & 54.27 & \textbf{63.46} &~~7.02 & 19.94 & 51.38          & 52.68          \\
  021\_bleach\_cleanser    & 49.41 & 42.54 & 56.03 & 38.52 & 42.54 & 46.29 & 51.54 & 61.44          & 16.70 & 22.94 & 53.50          & \textbf{63.62} \\
  024\_bowl                & 57.42 & 54.25 & 58.15 & --    & 54.25 & 57.05 & 60.30 & 69.40          & --    & 37.00 & 62.49          & \textbf{74.74} \\
  025\_mug                 & 40.68 & 37.28 & 43.70 & --    & 37.28 & 38.69 & 41.15 & \textbf{49.51} & --    & 18.21 & 34.87          & 48.48          \\
  035\_power\_drill        & 47.93 & 41.86 & 50.79 & --    & 41.86 & 45.79 & 58.10 & \textbf{64.28} & 19.60 & 30.67 & 51.41          & 49.22          \\
  036\_wood\_block         & 40.11 & 38.66 & 49.50 & 40.53 & 38.66 & 44.08 & 49.91 & 65.39          & --    & 23.80 & 51.63          & \textbf{67.14} \\
  037\_scissors            & 21.93 & 22.87 & 25.90 & --    & 22.87 & 25.29 & 27.48 & \textbf{32.65} & 14.16 & 17.18 & 29.40          & 24.36          \\
  040\_large\_marker       & 35.09 & 32.12 & 38.19 & --    & 32.12 & 38.73 & 33.27 & 46.63          & --    & 19.05 & 29.66          & \textbf{50.58} \\
  052\_extra\_large\_clamp & 30.48 & 31.89 & 34.42 & --    & 31.89 & 33.60 & 39.63 & 47.04          & --    & 22.99 & 35.99          & \textbf{50.78} \\
  061\_foam\_brick         & 12.80 & 13.04 & 15.75 & --    & 13.04 & 16.70 & 18.14 & 27.57          & --    & 17.40 & \textbf{32.42} & 30.12          \\
  \hline
  all                      & 41.65 & 38.26 & 45.18 & 37.41 & 38.26 & 43.27 & 48.99 & \textbf{57.54} & --    & 26.24 & 46.48          & 57.43
 \end{tabular}
 \vspace{-2mm}
 \caption{\small 6D object pose estimation results in AR (\%). Left: PoseCNN (RGB). Right: performance of representative approaches on S1.}
 \vspace{-2mm}
 \label{tab:6d:sota}
\end{table*}

We next evaluate on the four setups using the same baseline. Results are shown
in Tab.~\ref{tab:6d:sota} (left). On S1 (unseen subjects), we observe a drop in
AR compared to S0 (e.g., 38.26 versus 41.65 on all) as in object detection.
This can be attributed to the influence of interaction from unseen hands as
well as different grasping styles. The column of S3 (unseen grasping) shows the
AR of the three only objects being grasped in the test set (i.e.
``009\_gelatin\_box'', ``021\_bleach\_cleanser'', ``036\_wood\_block'').
Again, the AR is lower compared to on S0, and the drop is especially
significant for smaller objects like ``009\_gelatin\_box'' (33.07 versus
46.62). Surprisingly, AR on S2 (unseen views) does not drop but rather
increases slightly (e.g., 45.18 versus 41.65 on all). This suggests that our
multi-camera setup has a dense enough coverage of the scene such that training
on certain views can translate well to some others.

Finally, we benchmark five representative approaches:
PoseCNN~\cite{xiang:rss2018}, DeepIM~\cite{li:eccv2018},
DOPE~\cite{tremblay:corl2018}, PoseRBPF~\cite{deng:rss2019}, and
CosyPose~\cite{labbe:eccv2020} (winner of the BOP challenge 2020). All of them
take RGB input. For PoseCNN, we also include a variant with post-process depth
refinement. For DeepIM and PoseRBPF, we also include their RGB-D variants. For
CosyPose, we use its single view version. For DeepIM and CosyPose, we
initialize the pose from the output of PoseCNN (RGB). We retrain all the models
on DexYCB except for DOPE and PoseRBPF, which trained their models solely on
synthetic images. For DOPE, we train models for an additional 7 objects besides
the 5 provided. Tab.~\ref{tab:6d:sota} (right) compares the results on the most
challenging S1 (unseen subjects) setup. First, we can see a clear edge on those
which also use depth compared to their respective RGB only variants (for
PoseCNN, from 38.26 to 43.27 AR on all). Second, refinement based methods like
DeepIM and CosyPose are able to improve significantly upon their initial pose
input (e.g., for DeepIM RGB-D, from 38.26 to 57.54 AR on all). Finally,
CosyPose achieves the best overall AR among all the RGB-based methods (57.43 AR
on all).

\subsection{3D Hand Pose Estimation}
\label{sec:3d}

The task is to estimate the 3D position of 21 hand joints from a single image.
We evaluate with two metrics: mean per joint position error (MPJPE) in mm and
percentage of correct keypoints (PCK). Besides computing error in absolute 3D
position, we also report errors after aligning the predictions with ground
truths in
post-processing~\cite{zimmermann:iccv2017,zimmermann:iccv2019,hampali:cvpr2020}.
We consider two alignment methods: root-relative and Procrustes. The former
removes ambiguity in translation by replacing the root (wrist) location with
ground thuths. The latter removes ambiguity in translation, rotation, and
scale, thus focusing specifically on articulation. For PCK we report the area
under the curve (AUC) over the error range $[0,50~\mathrm{mm}]$ with 100 steps.

We benchmark one RGB and one depth based approach. For RGB, we select a
supervised version of Spurr et al.~\cite{spurr:eccv2020} which won the HANDS
2019 Challenge~\cite{armagan:eccv2020}. We experiment with the original
ResNet50 backbone as well as a stronger HRNet32~\cite{sun:cvpr2019} backbone,
both initialized with ImageNet pre-trained models and finetuned on DexYCB. For
depth, we select A2J~\cite{xiong:iccv2019} and retrain the model on DexYCB.

\begin{table}[t]

 \centering
 \footnotesize
 \setlength{\tabcolsep}{4.3pt}
 \begin{tabular}{ll|C{1.52cm}C{1.52cm}C{1.52cm}}
                      & & absolute & root-relative & Procrustes \\
  \hline
  \multirow{3}{*}{S0} & ~\cite{spurr:eccv2020} + ResNet50 & 53.92 (0.307)                   & 17.71 (0.683)                   &~~7.12 (0.858)                   \\
                      & ~\cite{spurr:eccv2020} + HRNet32  & 52.26 (0.328)                   & \textbf{17.34} (\textbf{0.698}) &~~\textbf{6.83} (\textbf{0.864}) \\
                      & A2J~\cite{xiong:iccv2019}         & \textbf{27.53} (\textbf{0.612}) & 23.93 (0.588)                   & 12.07 (0.760)                   \\
  \hline
  \multirow{3}{*}{S1} & ~\cite{spurr:eccv2020} + ResNet50 & 70.23 (0.240)                   & 22.71 (0.601)                   &~~8.43 (0.832)                   \\
                      & ~\cite{spurr:eccv2020} + HRNet32  & 70.10 (0.248)                   & \textbf{22.26} (\textbf{0.615}) &~~\textbf{7.98} (\textbf{0.841}) \\
                      & A2J~\cite{xiong:iccv2019}         & \textbf{29.09} (\textbf{0.584}) & 25.57 (0.562)                   & 12.95 (0.743)                   \\
  \hline
  \multirow{3}{*}{S2} & ~\cite{spurr:eccv2020} + ResNet50 & 83.46 (0.208)                   & \textbf{23.15} (\textbf{0.566}) &~~\textbf{8.11} (\textbf{0.838}) \\
                      & ~\cite{spurr:eccv2020} + HRNet32  & 80.63 (0.217)                   & 25.49 (0.530)                   &~~8.21 (0.836)                   \\
                      & A2J~\cite{xiong:iccv2019}         & \textbf{23.44} (\textbf{0.576}) & 27.65 (0.540)                   & 13.42 (0.733)                   \\
  \hline
  \multirow{3}{*}{S3} & ~\cite{spurr:eccv2020} + ResNet50 & 58.69 (0.281)                   & 19.41 (0.665)                   &~~7.56 (0.849)                   \\
                      & ~\cite{spurr:eccv2020} + HRNet32  & 55.39 (0.311)                   & \textbf{18.44} (\textbf{0.686}) &~~\textbf{7.06} (\textbf{0.859}) \\
                      & A2J~\cite{xiong:iccv2019}         & \textbf{30.99} (\textbf{0.608}) & 24.92 (0.581)                   & 12.15 (0.759)
 \end{tabular}
 \vspace{-2mm}
 \caption{\small 3D hand pose estimation results in MPJPE (mm). Numbers in
parentheses are AUC values.}
 \vspace{-3mm}
 \label{tab:3d}
\end{table}

Tab.~\ref{tab:3d} shows the results. For~\cite{spurr:eccv2020}, we can see that
estimating absolute 3D position solely from RGB is difficult (e.g., 53.92 mm
absolute MPJPE with ResNet50 on S0). The stronger HRNet32 backbone reduces the
errors over ResNet50 but only marginally (e.g., 52.26 versus 53.92 mm absolute
MPJPE on S0). Similar to in object pose, the errors on S1 (unseen subjects)
increase from S0 (e.g., 70.10 versus 52.26 mm absolute MPJPE for HRNet32) due
to the impact of unseen hands and grasping styles. However, unlike the trend in
Tab.~\ref{tab:6d:sota} (left), the errors on S2 (unseen views) also increase
pronouncedly and even surpass the other setups (e.g., 80.63 mm absolute MPJPE
for HRNet32). This is because unlike objects, the subjects are always facing
the same direction from a situated position in this setup. This further
constrains the possible hand poses observed in each view, making cross view
generalization more challenging. Unsurprisingly, A2J~\cite{xiong:iccv2019}
outperforms~\cite{spurr:eccv2020} on absolute MPJPE due to the depth input, but
falls short on estimating articulation as shown by the errors after alignment
(e.g., 12.07 versus 6.83 mm Procrustes MPJPE for HRNet32 on S0).

\section{Safe Human-to-Robot Object Handover}

\paragraph{Task} Given an RGB-D image with a person holding an object, the goal
is to generate a diverse set of robot grasps to take over the object without
pinching the person's hand (we refer to as ``safe'' handovers). The diversity
of grasps is important since not all the grasps are kinematically feasible for
execution. We assume a parallel-jaw Franka Panda gripper and represent each
grasp as a point in $\textit{SE}(3)$.

\begin{figure}[t]

 \centering
 \includegraphics[width=0.91\linewidth]{grasp_pc_curve-precision_coverage_s1_test_camera_ready.pdf}
 \vspace{-2mm}
 \caption{\small Precision-coverage curves for grasp generation on S1.}
 \vspace{-2mm}
 \label{fig:curve}
\end{figure}

\vspace{-3mm}
\paragraph{Evaluation} We first sample 100 grasps for each YCB object using
farthest point sampling from a diverse set of grasps pre-generated for that
object in~\cite{eppner:isrr2019}. This ensures a dense coverage of the pose
space (Fig.~\ref{fig:grasp}). For each image, we transform these grasps from
the object frame to camera frame using the ground-truth object pose, and remove
those collided with the ground-truth object and hand mesh. This generates a
reference set of successful grasps $\mathcal{R}$.

Given a set of predicted grasps $\chi$, we evaluate its diversity by computing
the \textit{coverage}~\cite{eppner:isrr2019} of $\mathcal{R}$, defined by the
percentage of grasps in $\mathcal{R}$ having at least one matched grasp in
$\chi$ that is neither collided with the object nor the hand. Specifically, two
grasps $g$, $h$ are considered matched if $|g_{t}-h_{t}|<\sigma_{t}$ and
$\arccos(|\langle g_{q},h_{q}\rangle|)<\sigma_{q}$, where $g_{t}$ is the
translation and $g_{q}$ is the orientation in quaternion. We use
$\sigma_{t}=0.05~\mathrm{m}$ and $\sigma_{q}=\ang{15}$.

One could potentially hit a high coverage by sampling grasps exhaustively.
Therefore we also compute \textit{precision}, defined as the percentage of
grasps in $\chi$ that have at least one matched successful grasp in
$\mathcal{R}$.

\vspace{-3mm}
\paragraph{Baseline} We experiment with a simple baseline that only requires
hand segmentation and 6D object pose. Similar to constructing $\mathcal{R}$, we
transform the 100 grasps to the camera frame but using the estimated object
pose, then remove those that are collided with the hand point cloud obtained by
the hand segmentation and the depth image. Specifically, a grasp is collided if
the distance of a pair of points from the gripper point cloud and the hand
point cloud is less than a threshold $\epsilon$. The gripper point cloud is
obtained from a set of pre-sampled points on the gripper surface. We use the
hand segmentation results from Mask R-CNN (Sec.~\ref{sec:2d}).

\vspace{-3mm}
\paragraph{Results} We evaluate grasps generated with different object pose
methods at different threshold $\epsilon\in[0,0.07~\mathrm{m}]$ and show the
precision-coverage curves on S1 in Fig.~\ref{fig:curve}. We see that better
object pose estimation leads to better grasp generation. Fig.~\ref{fig:grasp}
shows qualitative examples of the predicted grasps. We see that most of the
failure grasps (red and gray) are due to inaccurate object pose. Some are
hand-colliding grasps caused by a miss detected hand when the hand is partially
occluded by the object (e.g., ``003\_cracker\_box''). This can be potentially
addressed by model based approaches that directly predict the full hand shape.

\begin{figure}[t]

 \centering
 \includegraphics[width=0.32\linewidth]{100grasps-019_pitcher_base.png}
 \includegraphics[width=0.32\linewidth]{100grasps-011_banana.png}
 \includegraphics[width=0.32\linewidth]{100grasps-003_cracker_box.png}
 \includegraphics[width=0.32\linewidth]{grasp_quality-040712_overlay_camera_ready.png}
 \includegraphics[width=0.32\linewidth]{grasp_quality-028420_overlay_camera_ready.png}
 \includegraphics[width=0.32\linewidth]{grasp_quality-047710_overlay_camera_ready}

 \caption{\small Top: 100 successful grasps sampled
from~\cite{eppner:isrr2019}. Bottom: predicted grasps generated by the
predicted object pose (textured model) and hand segmentation (blue masks).
Green ones denote those covering successful grasps, red ones denote those
collided with the object or hand, and gray ones are failures not covering any
successful grasps in the reference set. Ground-truth objects and hands are
shown in translucent white and brown meshes.

 }
 \vspace{-2mm}
 \label{fig:grasp}
\end{figure}

\section{Conclusions}

We have introduced DexYCB for capturing hand grasping of objects. We have shown
its merits, presented a thorough benchmark of current approaches on three
relevant tasks, and evaluated a new robotics-relevant task. We envision our
dataset will drive progress on these crucial fronts.

{\small
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


Bregman received six of the 11 first-place votes.
The very same day the Houston Astros chose infielder Alex Bregman No. 2 overall in the 2015 draft, they promoted shortstop Carlos Correa – the top pick three years earlier – to the majors to stay.
So Bregman wasn't going to remain at shortstop, his position in college at LSU. He also wasn't going to play regularly at second base, where some scouts envision him eventually winding up, because that spot is taken by former batting champion and current MVP candidate Jose Altuve.
And while third base seems like a natural fit, and indeed Bregman has shined defensively at his new home since being called up July 25, the Astros did grant a team-high $47.5 million contract this summer to Cuban infielder Yulieski Gurriel, who was supposed to be ticketed for the hot corner.
All of which has created considerable consternation in Houston regarding the Astros' plans for their top prospect. The worried parties, however, don't include Bregman, who has taken to third base as if he'd learned it from Brooks Robinson.
That's the kind of attitude that convinced the Astros they couldn't pass up on Bregman, who has rewarded their trust with a quick ascent to the majors while earning USA TODAY Sports' Minor League Player of the Year honors.
He follows in a recent pattern set by current major league stars Kris Bryant (the 2014 winner) and Xander Bogaerts (2013), who preceded left-hander Blake Snell of the Tampa Bay Rays, the 2015 winner.
Like Bregman, Bogaerts is a natural shortstop who first made his mark helping the Boston Red Sox claim the 2013 World Series as a rookie third baseman, before eventually switching back to short.
Bryant, a third baseman by trade, has emerged as a top MVP candidate for the Chicago Cubs not only through his power hitting but his versatility as well. Bryant, among the National League leaders in home runs and RBI, has played at least 35 innings at four positions – third, left field, right field and first base – and even made a brief appearance at short.
For now, it appears the Astros will stick with Bregman at third while Gurriel gets most of his at-bats at DH and playing left field, then they'll formulate a longer-term plan in the offseason.
Already, they have seen signs of the maturity and intangibles Bregman exhibited as a two-time All-American and a Golden Spikes finalist at LSU.
After he put on a show with a single, double and triple at the Futures Game, Bregman continued pounding minor-league pitching, compiling 20 homers and a .986 on-base-plus-slugging percentage until the Astros called him up July 25.
Then his bat went quiet. Bregman went hitless in his first 18 at-bats, and after eight games he was 1-for-32.
Had the Astros called him up too soon? Would the slump scar him?
Mike Elias, Houston's director of amateur scouting, said the team had no concerns he could handle it and start hitting again.
That determination was evident when Bregman requested No. 30 at LSU as a reminder that all 30 major league teams had passed on him several times in the draft when he came out of high school in Albuquerque, N.M., in 2012.
Never mind that he had missed most of his senior season with a hand fracture and had indicated his intention to go to college unless he was picked in the first round. The Boston Red Sox eventually took a flyer in the 29th round but didn't sign him.
Bregman seems to be proving it with his performance after the early slump, which he followed by batting .325 with seven homers and an .969 OPS in the next 30 games.
Bregman has shown an ability to adapt, not only switching positions but also altering his approach to try to pull pitches in the middle-to-inside part of the plate and hit them in the air, a particularly good idea at Minute Maid Park with its short fence in left field. Bregman attributes that change and a weight gain of nearly 20 pounds in the offseason to his power surge, after he hit a total of 21 homers in his three-year college career.
His knack for making adjustments should come as no surprise considering his baseball bloodlines. Bregman's father and uncle played the game at the University of New Mexico, and his grandfather, the late Stanley Bregman, was legal counsel for the Washington Senators and was involved in hiring Ted Williams as their manager.
That might be a bit ambitious, but after the rough start, Bregman is demonstrating he can be a major contributor – wherever the Astros choose to play him.
The USA TODAY Sports Minor League Player of the Year was selected through voting on a pool of finalists by USA TODAY Sports writers and editors plus readers at usatoday.com. Each staffer's vote counted for one point. The winner of the online poll (Alex Bregman) received two points.


In February 2017 Knowlex working with NHS Improvement, NHS England and Public Health England brought together over 400 attendees to explore the initiatives around GNBSI and Sepsis announced by Jeremy Hunt and Sir Bruce Keogh in 2016.
In London on the 21st February of 2018 we will meet again to share new data on Sepsis and AMR CQUIN performance. We will also review GNBSI Quality Premium performance across 2017-18 and lots of learning from success to share moving forward into 2018-19.
"The NHS can rightly be proud that in the last 6 years we've reduced the number of MRSA cases by 57% and C. difficile by 45%. These aren't abstract numbers – they show that we have prevented the needless suffering – sometimes fatal suffering – of over 60,000 people in that period. Because every avoidable infection also has a financial cost, we know that progress has also saved the NHS over half a billion pounds".
On the 21st February of 2018 we will meet again at the Brewery in London to review progress and results against these new targets and strategies. We will also celebrate local and national successes and identify opportunities within the CQUIN programme.
Reducing Gram Negative Blood Steam Infections - Is it possible?
Workshop Stream 7: To foam or not to foam? Ideal Detergents For Reprocessing Medical Devices.
Travelling to the Brewery by public transport is quick and easy; situated in the heart of the City within walking distance of several Underground and Mainline train stations.
Circle, Metropolitan, Hammersmith & City, Central and mainline rail network.
A network of bus routes serve the local area. Please call London Transport on: 020 7222 1234 for details of your best route.
Bicycle parking is available at the Brewery. Bicycle helmets can be left at our complimentary cloakroom.


Successful companies are the ones whose leaders ask "Is there a better way?" Pursuit of the answers is usually the key to success and growth. That approach to business has been the underlying theme for Protec Dental Laboratories, one of Canada's largest dental labs. Protec, located in Vancouver, is a full-service lab providing orthodontic, restorative, and fixed and removable prosthetics and implants. Since its start, Protec has made innovation and a healthy curiosity for new technology the bedrock of its own success and growth, with 3D printing at the heart of it all.
A view of the lab at Protec Dental.


vMware No longer implementing 2 year Renewal SCAM for SysAdmins!
This comment was posted to reddit on Feb 06, 2019 at 10:33 pm and was deleted within 13 minutes.
People have lots of negative opinions on the certification industry but it is particularly bad with VMware.
You can sink up to a thousand dollars a day in a four day course for the privilege. I'd love to know what kickback VMware get for certifying these training vendors.
I can get a Microsoft action pack and run an entire small IT company on it for a few hundred bucks. Get bent over by VMware to sell their product and you can expect to pay thousands of dollars a year for Internal use licensing.
I benefited from this, I've had my VCP since VCP4 and really didn't want to renew. And I still feel angry about their whole position.
union for technical support specialist and systems administrators? Imaging more than 100 Surface Pros at the same time. How do you organize the deployment? (Story Time) Lying is not okay, rules DO apply to you, and good senior management is worth it's weight in platinum Lyft's AWS $8 Million per month price tag Moronic Monday - February 18, 2019 6 Hours of troubleshooting Is it just me, or is IT complexity proceeding at an unmanageable rate? Dotcom Bubble 2.0 is here -- CS enrollment is going crazy again... How do you deal with the self importance and entitlement of higher-ups? Anyone else tired of hearing about DevOps? Cable Management Tips/Advice Request 43,000+ failed login attempts. Is this normal?? Products from ZTE or Huawei It's Black Friday. If you're a IT admin for retail, knee deep into a crisis right now, please read this. Suggest a title for me!


text:
Microsoft's Windows Nano Server is for DevOps devotees
The new reduced version of Windows Server is only for those committed to DevOps and containers
Simon Bisson
Microsoft's reduced and refactored Nano Server offers lots of performance improvements for cloud and DevOps, and represents the culmination of the operating systems progress towards a different approach to administration.
The new, tiny Nano Server, announced this week, promises significant improvements – a 93 percent lower VHD size, 92 percent fewer critical bulletins, and 80 percent fewer reboots. It also caps several years in which Microsoft has redirecting its system administration tools, away from local GUIs to the PowerShell command line tooling.
All of this is a big change, and one that will make system administrators lives easier, letting them focus on improving services rather than maintaining infrastructure.
Mike Neil, Microsoft Windows Server general manager – Microsoft
Docker as well
Nano Server wasn't Microsoft's only announcement; it's also increased its focus on containers, forking Docker to add Windows support and announcing a Hyper-V hosted container model.
We spoke to Mike Neil, general manager for Windows Server, about the company's announcements and what they mean for the modern data center – and for Microsoft's approach to delivering software-defined infrastructure.
The new version includes some very signficant changes, Neil explained: "As we did the refactoring work in Nano server we went back historically and looked at what caused reboots, what are the dependencies, what are the pieces of functionality for a server that are frankly not paramount capabilities? A lot of refactoring was driven by how to reinvent that. There was a trade-off, to make sure it can run apps, provide functionality, and provide the necessary infrastructure to build the cloud."
In practise, this has meant removing the entire GUI stack as well as ending 32-bit support. You won't be able to log on locally, or use remote desktop: everything must be managed through remote PowerShell and its Desired State Configuration tools, as well as a set of new web-based management features.
Looking to the data center
What's perhaps most interesting about this set of announcements is its focus on the next generation data center; and on the tools and technologies that will be needed to deliver a software-defined infrastructure for scalable applications. Microsoft describes Nano Server as a platform for what it's calling "modern" applications, built using DevOps techniques and using scalable micro-service focused platforms like node.js.
It's certainly scalable. Neil told us that using Hyper-V with Nano Server it's possible to run more than 1000 virtual machine instances on a single host. High density applications like this work well as a way of delivering containerized microservices, with Docker Windows containers wrapping applications and services, and Nano Sever being used as a container host.
Scaling out Nano Server images should allow rapid re-configuration, with tools like System Center Orchestration working in conjunction with Docker-focused application orchestration tools like Kubernetes.
Trusting automation
That's where refactoring Windows Server comes into play. A GUI management platform is at best a distraction and at worst a hindrance when working with thousands of servers. Instead of the individual touch we've got so used to with Windows Servers, we need to use automation to work with cloud-scale systems. Microsoft is working with DevOps vendors like Chef to deliver automation tooling, and we won't be surprised to see Chef's recently announced Delivery workflow tools being used to handle Nano Server configurations.
In operating system research, there's a concept called the Library OS, where a minimal OS platform can be tuned with the specific services and features needed to deliver an application – reducing the need for updates, and keeping the attack surface as small as possible. Nano Server isn't that OS, but it's a long way down the road to delivering it. Using tools like PowerShell DSC and Chef, it's going to be possible to programmatically construct a server description that can build on Nano Server as a core, and add the elements needed to support a service as required – and remove them when that service is no longer required. According to Neil this approach will allow Nano Server to scale up its features significantly, "You have the option of going back to Server Core, but as you grow in footprint and in size, by adding this you take the impact of doing that."
Nano Server isn't the only component of this data center-scale approach to infrastructure. Microsoft's new Hyper-V Containers are a blend of the familiar hypervisor with the new application abstractions that come with tools like Docker. As Neil explains it, "We use the hypervisor to provide an isolation mechanism, the tried and tested use of virtualization based in a hardware root of trust. It's very much a core function of the hypervisor to provide that isolation. We then provide higher level abstraction for network and file systems within that boundary, blending the two together." That way you get the benefits of a highly isolated hardware solution, along with the higher level abstraction of containers, with more shared resources and less associated overhead. The Docker-like Windows Server container uses a shared kernel instance, so updating a container host requires taking all its instances down. With Hyper-V containers you can upgrade containers individually, without affecting the other services running on a host – more like a traditional virtualized environment.
Only for DevOps devotees
Flexibility is key to delivering a modern data center, and by using the combination of Nano Server and its new container technology Microsoft is making a big shift away from its previous monolithic server model to one that's more aligned with the way we deliver cloud-scale services. That does mean that Nano Server won't be for everyone. You're going to have to have made the shift to a DevOps model, and to using cloud-scale data center infrastructure practices.
It's not surprising that Microsoft described Nano Server as ideal for use with its CPS "cloud-in-a-box" rack-scale systems. As Neil notes, "With CPS as our reference architecture, all our technologies for our cloud stack are integrated together as a fully integrated solution. Nano Server would play into future version, we'd have more densities and we'd have more versatility. There's a lot of goodness we'll get from using Nano server as part of our solution."
software defined infrastructure
Who wants to be a data center engineer?
How the Covid-19 pandemic has changed the data center business
Building energy efficient data centers in the tropics
The PlayStation Supercomputer
Sponsored Content How 5G will affect the structure of data centers

redpajama_set_name: RedPajamaCommonCrawl


Medical Billing and Coding - Procedure code, ICD CODE.: AR Analysis in Medical Billing..
AR Analysis in Medical Billing..
Welcome to one of the key functionalities, and the biggest department of Medical Billing, in other words, the Accounts Receivables (AR) Department. It is the pivot department, which makes the other departments revolve around it. It can also be called as the back-bone of medical billing. It is the financial department of Medical Billing as only this department deals with the payment of the claims, and acts on the receivables that are outstanding to bring in the money. It is only based on this department, the revenue of the Doctor as well as the Billing Company is determined. The more proactive and aggressive is this department, the more the revenue will be. So let's talk about this department.
Accounts Receivables is broadly classified into two sub-departments, they are AR Analysis and AR Calling. Let's look at AR Analysis briefly.
Analysis is the most important part of billing. An AR Analyst is a person, who monitors the receivables to ensure that it is well within control. He should also keep in mind that the main objective of a billing company is to maximize collections. He should work towards the common goal and create methods to make sure the goal is attained. For this purpose, the analyst should set targets every month of what the collections would be for that month taking into account various factors such as the average turnaround time and unresolved issues. Therefore there are two major functions of an analyst – Maximizing collections and reducing the receivables.
1. Constantly keep track of both electronic and paper claims.
2. Always be watchful for any major rejections or denials –clearing house/carrier.
3. Constantly watch-out for payments and EOBs from major carriers, Pay-to address, provider numbers etc.
4. Ensure the AR days meet industry standards.
5. Co-ordinate with the Call Center crew, Client co-ordination and solve problems.
6. Ensuring the compliance with all the insurance carriers in claims submission and other areas.
1. Daily Reports: This report gives the value of charges, insurance receipts, patient receipts, write-offs and charge adjustments and the number of procedure units entered on a particular accounting date and the gross AR as on that date for the Doctor.
2. Production Reports: This report shows the number of demographics, charge, and cash entered by the data entry staff for the calendar date specified.
3. Month End Reports and Financials: This report gives the total summary of the charges entered, cash received, outstanding payment, total Doctor's revenue of that month, and the comparison chart showing the payment of the previous month, statistics, and the whole of that month's payment, and collections. This report must be sent to the Doctor at the end of every month.


text:
Jamie Lynn Spears on Why She Decided to 'Hide Away' After Announcing Pregnancy at 16
"There's a human being growing inside of me, and I'm so young, I'm almost a baby myself," Spears recalled thinking at the time
By Gina Vivinetto | TODAY	• Published October 20, 2020 • Updated on October 20, 2020 at 1:00 am
Peter Kramer/NBC/NBC NewsWire
Jamie Lynn Spears is opening up to Nylon about her decision to avoid the spotlight after announcing she was pregnant at 16.
The former child star, 29, went public with her pregnancy news in 2007 shortly after wrapping filming on the wholesome Nickelodeon series "Zoey 101."
"You have your first love, or what you call love in high school. You think it's forever, and then oh my gosh, I'm pregnant," Spears recalled thinking at the time.
"I am mortified to have to tell my parents this and my family this. I do not care what TMZ thinks about it. That was all I was thinking about: 'These are the people I love that I have to tell, and also, I'm pregnant. There's a human being growing inside of me, and I'm so young, I'm almost a baby myself,'" she continued.
Los Angeles 2 hours ago
"I didn't really care about any of that nonsense, but when we did announce that I was pregnant, I did hide away for a little bit during that time and I didn't have a phone," she added. "But of course, the paparazzi still found me. It's not like I was in L.A. walking down the middle of the most popular boulevard; I was in the-middle-of-nowhere Mississippi."
Spears welcomed daughter Maddie, now 12, with her ex-boyfriend Casey Aldridge. She's also mom to a 2-year-old daughter, Ivey Joan, with husband Jamie Watson.
The "Sweet Magnolias" star explained that she moved back to small-town Mississippi while pregnant with Maddie to try to provide some "normalcy" for her baby-on-the-way.
"So, I got me a little house. I put a big gate up around it, and I was like, 'I'm going to stay here, raise my baby, and figure this out because this is real life. I've put myself in this situation, I'm not condoning it or saying it's right, but these are the cards that I have to play,'" said Spears. "And I tried to do the best that I could."
As tabloid headlines lambasted her for being an irresponsible role model, Spears knew her decision to temporarily leave Hollywood was the right one. "I just knew that if I just stayed away from it long enough that I could give my child some sense of normalcy back in her life."
Spears grew up watching her older sister, pop music icon Britney Spears, become one of the most famous women in the world. Looking back, she's grateful Britney was always "open" with her about the pitfalls of fame.
"She taught me to protect myself," explained Jamie Lynn Spears. "And I think that I learned how to be strong, but also keep it honest, and it was 100 percent helpful in every single way."
This story first appeared on TODAY.com. More from TODAY:
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Naomi Campbell talks about racism and the strong women who raised her
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Eye Could Provide "Window to the Brain" After Stroke
Eyes yield information about strokes: MRI scans revealed that a chemical called gadolinium, used to improve images, leaked into the eyes of stroke patients.NINDS Stroke Branch
Research into curious bright spots in the eyes on stroke patients' brain images could one day alter the way these individuals are assessed and treated. A team of scientists at the National Institutes of Health found that a chemical routinely given to stroke patients undergoing brain scans can leak into their eyes, highlighting those areas and potentially providing insight into their strokes. The study was published in Neurology.
"We were kind of astounded by this – it's a very unrecognized phenomenon," said Richard Leigh, M.D., an assistant clinical investigator at the NIH's National Institute of Neurological Disorders and Stroke (NINDS) and the paper's senior author. "It raises the question of whether there is something we can observe in the eye that would help clinicians evaluate the severity of a stroke and guide us on how best to help patients."
The eyes glowed so brightly on those images due to gadolinium, a harmless, transparent chemical often given to patients during magnetic resonance imaging (MRI) scans to highlight abnormalities in the brain. In healthy individuals, gadolinium remains in the blood stream and is filtered out by the kidneys. However, when someone has experienced damage to the blood-brain barrier, which controls whether substances in the blood can enter the brain, gadolinium leaks into the brain, creating bright spots that mark the location of brain damage.
Previous research had shown that certain eye diseases could cause a similar disruption to the blood-ocular barrier, which does for the eye what the blood-brain barrier does for the brain. Dr. Leigh's team discovered that a stroke can also compromise the blood-ocular barrier and that the gadolinium that leaked into a patient's eyes could provide information about his or her stroke.
"It looks like the stroke is influencing the eye, and so the eye is reflective of what is going on in the brain," Dr. Leigh said. "Clearly these results are preliminary, so future studies will have to be attuned to this to fully understand its impact."
The researchers performed MRI scans on 167 stroke patients upon admission to the hospital without administering gadolinium and compared them to scans taken using gadolinium two hours and 24 hours later. Because gadolinium is transparent, it did not affect patients' vision and could only be detected with MRI scans. Roughly three-quarters of the patients experienced gadolinium leakage into their eyes on one of the scans, with 66 percent showing it on the two-hour scan and 75 percent on the 24-hour scan. The phenomenon was present in both untreated patients and patients who received a treatment, called tPA, to dissolve the blood clot responsible for their strokes.
Gadolinium was typically present in the front part of the eye, called the aqueous chamber, after two hours, and in a region towards the back, called the vitreous chamber, after 24 hours. Patients showing gadolinium in the vitreous chamber at the later timepoint tended to be of older age, have a history of hypertension, and have more bright spots on their brain scans, called white matter hyperintensities, that are associated with brain aging and decreased cognitive function.
In a minority of patients, the two-hour scan showed gadolinium in both eye chambers. The strokes in those patients tended to affect a larger portion of the brain and cause even more damage to the blood-brain barrier than the strokes of patients with a slower pattern of gadolinium leakage or no leakage at all. The findings raise the possibility that, in the future, clinicians could administer a substance to patients that would collect in the eye just like gadolinium and quickly yield important information about their strokes without the need for an MRI.
"It is much easier for us to look inside somebody's eye than to look into somebody's brain," Dr. Leigh said. "So if the eye truly is a window to the brain, we can use one to learn about the other."
Despite the relationship between gadolinium leakage and stroke severity, the phenomenon was not found to be related to the level of disability the patients developed following their strokes. It also remains unclear whether gadolinium can enter the eye in healthy people.
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A couple of images of the Manhtattan skyline with the new Hudson Yards neighborhood made with a panoramic head and stitching software PTGUI.
I had the opportunity to photograph the opening ceremony for the Hudson Yards neighborhood and be among the first people to walk on the Vessel, the new public park in the middle of he Hudson Yards.
Yankees Magazine asked me to document a game day at Yankee Stadium. From the grounds crew getting the field ready, the players arriving in the tunnels, the fans arriving by train to all the sights around the stadium during the game.
The 2017 Robin Hood Benefit Gala with performances by Miley Cyrus, Dave Chappelle and Coldplay.
In the heart of Manhattan, a 7 acre green roof sits on top of the busiest convention center in the United States. In one small corner of that green roof, employees at the Jacob Javits Convention Center are taking advantage of the urban oasis to raise honey bees.
A recent assignment brought me to Trenton, NJ where the pitching staff for the Yankees AA team, the Trenton Thunder, has been having a lot of success as of late. I was able to spend a game day with Pitching Coach Jose Rosado and his stable of young arms.
Nothing like a great sunset while you are at work.
The view from the upper deck in right field of Yankee Stadium.
I recently had the opportunity to travel to a very remote village in Nicaragua to document a great project sponsored by Viridian Energy. After hours in the car, 4 wheeling over some of the toughest roads I have ever been on, we hiked for an hour into the village of Potrero Platanal. Due to their remote location, the village of subsistence farm families has never had access to electricity. Partnering with GRID Alternatives, a nonprofit organization that provides off-grid solar power to rural communities in Nicaragua, the volunteers from Viridian installed solar panels and light in 40 village home. It was a great experience and a pleasure to work with some wonderful people.
With Derek Jeter's career coming to a close, the Yankees final game of the season will be at Fenway Park. Here are a few of the images I made for a story about the Captain's career at Fenway and the park itself. Some of my favorites are the impressions the baseball leaves on the Green Monster, something Boston based photographer CJ Gunther has been photographing for a long time. To see some very cool images of the Green Monster on his website click here.
Yankees magazine used my photo of Carlos Beltran getting a Gatorade bath from Brett Gardner in the "On Deck" section as a double truck.
Thanks to Style Me Pretty for featuring an engagement shoot I did with a great couple.
A short video portrait of the Queensboro Bridge.
Time lapse fun with the New York Waterways ferries on the Hudson River.
Recently I had the opportunity to work with the legendary banjo player Tony Trischka. To help launch his new album, Great Big World, We documented a day in his recent tour. Throughout my career the thing I have enjoyed the most is the chance to be around greatness. Be it medicine, sports, art, music or any other field, greatness is an inspiring thing to see and I got to hang with a whole lot of it for a day. The stop motion and time lapse piece we created is now featured on the home page of Tony's website. Check it out and check out his new album.
2013 was a year of major change in my career. Leaving a staff job at a newspaper that I worked at since 1999 meant starting over in a lot of ways. Looking back at what I shot over the past 12 months I put together a group of 13 of my favorite images from the year. Someone else may have chosen 13 totally different photos, but here is what I came up with in no particular order.
While still on staff at the Daily News, I was assigned to a story about a new real estate development in the Hamptons on the eastern end of Long Island. The real kicker was our mode of transport. We flew in a sea plane out of the East River to get there. This was my view down at LaGuardia as we flew over.
Game 1 of the NBA playoff series between the New York Knicks and Indiana Pacers. I was fortunate to catch Kenyon Martin's reaction after a dunk with my remote camera. This 1 dunk yielded 3 or 4 good photos from the remote, but this one is my favorite.
The skies opened up over Yankee Stadium and this was my favorite image of Hiroki Kuroda walking off for the rain delay.
The assignment from Bloomberg News was to document the Maple Ridge Wind Farm in upstate New York. There is something about the juxtaposition of a horse and buggy and wind turbines that I like.
I was asked by the New York Times to photograph a race horse named Deweycolorintheline at the farm where she trains in Orange County, NY. It was a great assignment and I had so much fun on it. I like this detail photo from the horses bath.
The assignment from Bloomberg News was to photograph Metro North Commuters. I shot in a bunch of locations for this but this image of the train emptying out at Grand Central Terminal stood out to me.
I was sent to shoot Army play Stanford for Getty Images. West Point is an amazing place to visit and a great venue to shoot a college football game.
The thing I like most about this photo is looking at the faces of all the people in the crowd. Especially the guy right over the ball taking it upon himself to make sure no one interferes.
This portrait of Kevin Garnett was made during media day for the Brooklyn Nets. You are given a very brief amount of time with each player, sometimes less than a minute. It is great when you have a guy like Garnett who knows the show and gives you exactly what you need in the first 10 seconds.
The light was sneaking though an opening in the stadium and coming right at us. It was one of those times when you really wish you were not stuck in a photo well. It would have been nice to be able to move around a little more to work with the light. I just like the feeling the light gives to this photo.
Victor Cruz and the rest of the New York Giants take the field before the start their game against the Denver Broncos.
The same Bloomberg News assignment to photograph Metro North commuters allowed me the opportunity to shoot through an open window in the glass hallway that runs across the top of Grand Central Terminal.
The assignment was great. Make interesting aerial photos of New York. I used my tilt shift to isolate the span of the bridge and the flag from the background.
I had the opportunity to shoot from a helicopter the other day above New York City. One of those days that I am very happy with my career choice. Here are a few of my favorites from the day.
Here is a very quick time lapse of the Twitter banner going up on the New York Stock Exchange the night before the company's initial public offering.
Media day for the Brooklyn Nets and the chance to do portraits of some great athletes.
I was assigned to photograph a college football game between Army and Stanford. While of course I had to cover the action and story of the game, it was the scenery and atmosphere at Michie Stadium that appealed to me more. Here are a few non-action photos from the game. See if you can spot the photographer in one of the photos.


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EDM Wouldn't Exist if Wasn't for Hip Hop
Exclusives, feature, From the Issue, Hip Hop Music | Listen To and Download Hip Hop Tracks
Kap G Teams Up With Atlanta Hawks
haleemakhan
featured, News Exclusives, Source Latino | Latin Music News and Entertainment, Source Sports | Sports News
The Atlanta Hawks Basketball Club announced several promo events to highlight the diversity of the city of Atlanta. On March 13 it will also include a live performance by Kap G. The events will take place before the team's second annual Unity Game on march 15 against the Charlotte Hornets.
Kap G will also be contributing with an upcoming performance as part of the team's in-game concert series. The College Park, Georgia, born artist, who scored a gold-certified single with his song "Girlfriend", will perform during the diverse event series leading to the second annual unity game.
Known for rapping about his Mexican heritage, Kap G's performance supports the National Basketball Association's "Noches Éne-Bé-A" initiative, celebrating NBA fans and players across Latin America and U.S. Latins communities. That night the in-arena Hawks Shop and HawksShop.com will offer co-branded Kap G merchandise for sale as well. It will be a limited-edition "It's a Marvelous Day" themed T-shirt. "It's a Marvelous Day" is Kap G's latest single from his new EP Mood.
"Being from 'The A,' this opportunity means the world to me," said Kap G. "I love my Latin heritage and it's an honor that I'll get to share that with Hawks fans and my city. I want all of my fans to come out to the game and help me celebrate this historic moment."
atlantaHawksKap G
Haleema Khan is a writer from Toronto, ON, who also contributes to RESPECT and Boi-1da.net – A dedicated tastemaker, she also performes rare DJ sets on the regular.
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