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Statement on the Criminalization of Protest
The Criminalization of Protest
By Rev. Jimmie Hawkins, Director of the Office of Public Witness PC(U.S.A.), and Langley Hoyt, Fellow for Domestic Issues
In the weeks following the death of George Floyd, as protestors continue to fill the streets with largely peaceful demonstrations, police continue to retaliate with shows of force and militarization. The violence police use against Black Lives Matter protestors stems from a long history of police attacking marginalized groups advocating for their rights. This is the modern-day iteration of a historical pattern of police violence against peaceful protest. To protect the civil rights of the American people, the police must be reoriented to adopt more appropriate measures in response to demonstrations which pose no threat of violence.
Rather than moving to protect the rights of peaceful protestors, lawmakers in various states have passed 23 laws criminalizing and restricting the right to protest. These laws further protect violent police forces while allowing the prosecution of Americans for practicing their first amendment rights. This is a move in the wrong direction as local police departments must be de-militarized. De-militarization calls for an end to the transference of military equipment along with additional levels of staff training in de-escalation. Police should be accompanied by experienced staff with expertise in de-escalation as well as mental health proficiency. Officers should be able to deliver first aid assistance to those injured by their actions and undergo an independent investigation whenever deadly force is used. Information on officers with a history of violence towards the public should be shared if they are fired and reapply elsewhere.
The use of police violence to silence dissenting voices of protest is a pattern woven into the history of our country. In 1917, suffragettes who were peacefully picketing outside the White House were arrested, tortured, and imprisoned until the following year. Peaceful labor protests were suppressed by violence for over a century from 1874-1974. On July 28, WWI veterans, the Bonus Army, were battered, beaten and murdered by the U.S. Army when they set up tents in Washington D.C. demanding bonus pay. From Montgomery and Birmingham (Alabama) to Albany (Georgia), Civil Rights protests were met with violence and death. On "Bloody Sunday" in 1965, police attacked Civil Rights protestors in Selma, Alabama with high-power hoses and dogs. Martin Luther King Jr. described the Birmingham police force as using "gestapo-like" methods to combat protestors. After his murder, Lyndon B. Johnson pleaded with local governors and mayors to curtail police violence. According to the Smithsonian, he said, "I'm not getting through… They're all holing up like generals in a dugout getting ready to watch a war." Police killed a total of 9 college students and wounded 48 others during peaceful protests at Orangeburg College (February 8, 1968), Kent State University (May 4, 1970) and Jackson State College (May 15, 1970). In 2016, the police used pepper spray and tear gas against unarmed Indigenous activists protesting the Dakota Access pipeline. State-sanctioned violence against protestors has continued seamlessly from the early 1900s, through the Civil Rights Era, and into the current decade.
We must de-militarize the police to prevent the future mistreatment of peaceful protestors. This can be achieved by cutting funding to police budgets that provide inadequate training and military-grade weaponry. The money saved can be used to fund community programs proven to reduce crime and provide greatly needed resources to communities. Alex S. Vitale, professor of sociology, coordinator of the Policing and Social Justice Project at Brooklyn College, and the author of The End of Policing, recommended in The Guardian, "The alternative is not more money for police training programs, hardware or oversight. It is to dramatically shrink their function. We must demand that local politicians develop non-police solutions to the problems poor people face. We must invest in housing, employment and healthcare in ways that directly target the problems of public safety. Instead of criminalizing homelessness, we need publicly financed supportive housing; instead of gang units, we need community-based anti-violence programs, trauma services and jobs for young people; instead of school police we need more counselors, after-school programs, and restorative justice programs… Instead of giving them more money for pointless training programs, let's divert that money into building up communities and individuals so we don't 'need' violent and abusive policing."
Jesus himself de-escalated a violent encounter in the Garden of Gethsemane as he was arrested by Roman soldiers. Matthew 26:50-56 says, "Then (the soldiers) came and laid hands on Jesus and arrested him. Suddenly, one of those with Jesus put his hand on his sword, drew it, and struck the slave of the high priest, cutting off his ear. Then Jesus said to him, 'Put your sword back into its place; for all who take the sword will perish by the sword. Do you think that I cannot appeal to my Father, and he will at once send me more than twelve legions of angels? But how then would the scriptures be fulfilled, which say it must happen in this way?" At that hour Jesus said to the crowds, 'Have you come out with swords and clubs to arrest me as though I were a bandit? Day after day I sat in the temple teaching, and you did not arrest me. But all this has taken place, so that the scriptures of the prophets may be fulfilled.'"
As peaceful protests continue to be met by police violence, it is essential for Christians to recognize that they follow a person of color who was arrested and killed by Roman keepers of the law. Even as he was being led away to be crucified, Jesus encouraged those he met to be peacemakers rather than resorting to violence to solve the problems of their time. Jesus' message translates clearly into a calling for the current day: we must not use violence to uphold the power of the state, and we must advocate for a just world where peace is a reality for all.
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Stories from Tuesday, September 29, 2015
Methodist Church donates 10,800 packs of food to combat world hunger (Local News ~ 09/29/15)
Nevada Daily Mail The Nevada United Methodist Church helped feed those in the world hunger crisis after their church service, Sunday. Pam Hayes, president of the United Methodist Women, organized about 75 members of the congregation to pack meals for Stop Hunger Now with dehydrated meals comprised of rice, soy, vegetables, and essential vitamins and minerals. One package of food, which costs 29 cents, feeds six people...
Crain reflects on his training at the FBI National Academy (Local News ~ 09/29/15)
Nevada Daily Mail On Sept. 18, Nevada Police Capt. Casey Crain became the first member of the Nevada Police Department to graduate from the FBI National Academy in Quantico, Va., while a member of the department. Crain said that City Manager JD Kehrman recommended that he apply for a spot in the 10-week program for professional development for command-level staff in 2010 while he was serving as the interim chief...
CCPA changes play dates two weeks (Local News ~ 09/29/15)
Nevada Daily Mail Community Council on the Performing Arts is changing the performance dates for "Crimes of the Heart," by Beth Henley, originally scheduled to open Oct. 2 and run the first two weekends of October. "It has become necessary for us to partially recast and reschedule this play," stated a release from the CCPA...
Cottey holds ribbon cutting for new fine arts building (Local News ~ 09/29/15)
Lady Tigers win West Central Conference tournament (High School Sports ~ 09/29/15)
Richmond earns All-District golf honors (High School Sports ~ 09/29/15)
Nevada's Kaitlyn Richmond garnered All-District accolades after finishing in a tie for sixth place at the Class 1 District 4 Girl's Golf State Championships in Marshfield. The tournament was played Monday at Marshfield Country Club's 18-hole par 70 course. Richmond totaled a 93, shooting a 45 on the front nine, and a 48 on the back nine...
Cottey hosts seeding tournament (College Sports ~ 09/29/15)
Nevada Daily Mail The Cottey campus was consumed with volleyball Friday and Saturday, as the Comets hosted the NJCAA Region 16 seeding tournament. Six teams competed in the tournament, with a total of nine games being played. Friday, the Comets defeated St. Louis Community College in three sets 25-17, 25-19, and 25-16...

redpajama_set_name: RedPajamaCommonCrawl


Best WiFi Names is very hard to find, even the internet can make you confused in selecting the best one for your router. Most of the peoples have a personal WiFi router at their home or in office. But when we purchase a new Router, we like to search for the Funny WiFI Names for a router. Who doesn't? Everyone wants to become unique when someone tries to connect their WiFi. Are you one of those? If Yes then this article is like a Heaven for you. Because I am going to share some of the coolest Funny WiFi Names for Router in this article. All of your neighbors will get shocked after reading your WiFi Router name, this is my guarantee 😛 . Everyone wants to use a unique and best WiFi Name of all times but it is very hard to think the desired name you own. So, here I am helping you with a great collection of best WiFi Router Names.
Your WiFi name says a lot about you. We all have a wireless internet connection at our home or in office. Every WiFi has a name which is decided by its user. Most the of people likes to use Funny WiFi Names while others take the help of Internet in searching Best WiFi Names of All Times. In below section of this article, you will get your desired Dirty WiFi Names and many other special names for your WiFi Router. I have classified different categories so that you can directly scroll to your desired category for name selection. You may also like our shared Best Instagram Bios.
Best WiFi Names of All Times are here for those who are wasting their lot of time in thinking Funny WiFi Names. Are you searching for the same? If Yes then this article is like a heaven for you. We search for Best WiFi Names for Router after purchasing new WiFi system for our Home. Everyone wants to use something unique and amazing name for their router. Many of us want to make our neighbors jealous by using some negative names against them… Want an example? Okay, like "No Free WiFi For You" 😛 Isn't it interesting? I think your answer is Yes. There is plenty of Cool WiFi Names there but I have decided to share some Best WiFi Names of All Times with you. If you are Social Chatting Apps lovers, you may like our Whatsapp Group Names too which I shared some days ago.
After WiFi comes at your Home, you will be addicted to Social Media sites and apps like Facebook, etc. As on getting new WiFi, you set up its Name at first and same on Social Media Networking sites you will like to set up Stylish Boys Profile Pics or Girls DP if you are a girl… and if you are using Whatsapp, your next search is Whatsapp DP Images. 😛 Is there something wrong said by me? I don't think because I too did this all at starting. 😀 After these all settings, you come to set up the Name of your router. Everyone has the same nature so I already know you will surely search on the internet for the best Name suitable for your router.
We all love to use Best WiFi Names: Cool WiFi Names for our router. Many, people searched on Google for it what they got is amazing. The main reason behind using unique WiFi names for your router is, to shock your friends and neighbors. Whenever someone tries to Connect to your WiFi, he/she will get shocked just by reading your router name. :-p So, how's the idea? Yeah!! Its brilliant idea to use nerdy WiFi names. I guess, even you too get shocked many times after seeing hilarious WiFi names. Right? Yes, this world is so adventurous. Everyone tries to do something different and you are also one of them. That's why you are looking for Best WiFi Names for Router.
Whenever you set up new WiFi router to your home, first you think of some cool names for your WiFi. You think to set something special name which can be seen awesome by others who tries to access it without your permission. So, what if you will get funny router names collection in one place. If it's your wish, then this is the time to complete it. You may also like WhatsApp Dare games to play with your Friends. You should also check out YoWhatsApp & GBWhatsApp which are an amazing WhatsApp Mods ever.
Generally, people waste hours for thinking best Wifi names for their router and after long thinking, they choose one name for it. No matter it's the first name you remembered first. I simply mean that finally, you have successfully your 1 hour just for this small deal. Well, it is not an important task so I don't think you have to think enough about this. Well, you can take the help of internet in these type of situations. I am sure it will provide you the best solution for your query. Nowayads, the Internet is fulfilled with lots of useful articles. As we are talking about WiFi Names so YES, you can find these too. Oh wait, actually, you have already searched for it that's why you are here. :-p . Okay, you did correct, now it's our time to provide you with all the best WiFi Names of all times.
I think it's a bad idea to think best Wifi names for your router because it's time-consuming idea. Google is the best thinker for this type of situations. You also searched on Google and found my blog there. So I don't want to consume your more time in this type of paragraphs. Let's come to the point. So here I am going to sharing some of the Best WiFi names of all times. All type of names for your router is here, so let's choose the best name for your Wifi router and don't waste your time in thinking about this type of shits 🙂 . You should also check WhatsApp Groups Invite Links collection.
These are some Awesome Names for WiFi Router. I think you have got a suitable name for your WiFi router from the above-shared list. Because I have mentioned some of the best ever WiFi Names in this section. Well, no problem if you want some more suggestions. Just scroll down a bit and you will explore lots of more Wireless Router names in different categories including images suggestions too.
Interesting? 😀 Yeah, I know your answer is Yes. As it took lots of time to keep searching different Interesting WiFi Names over the internet. After a long search, we came to find out some amazing names for Routers which we have shared on the above list. Now, what are you waiting for? Copy your desired Name from the list an set it up on your router today. It's time to shock your neighbor with some shocking WiFi Names for neighbors.
These are some Dirty WiFI Names for dirty mind peoples :-p . Most of the boys have a dirty mind so they like to use these type of names on their router. If you really want to shock your neighbors then you should use a Name from the above list. As it is not an easy task to find these type of WiFi Names Dirty, so I have shared this to make it easy for you. You should also check out, Dolby Atmos Apk.
So, guys, these are the best WiFi names for router. You can apply these names to your Wifi router and your neighbors get shocked. Also, you can give an awesome reply to those who try to Hack or use your Wifi, I promise they feel super angry while searching for your Wifi :-p . Also, read How to Download Paid Apps for Free on Android.
I shared more than 500 cool Wifi names ever for your router. These were the best WiFi names of all times. You can cheers with your friend's group and in your society by applying these some of the best wifi names reddit on your router. Also, you can impress any girl by proposing her through your router's name :-p . I hope you loved my article. So if I am right, don't forget to share this cool article with your friends so that they can also make changes in their Wifi router name and insert some tweak in their life.
Let your neighbor feel jealous of you! You don't need to say anything to them just burn than with your Funny WiFi Name. I have shared lots of Funny WiFi Names of all time which are much liked by our readers. Still, I am updating more new cool WiFi Router Names in short interval of time. Share these amazing WiFi Router Names with your friends too!! If your mind is so creative and it stored some Best WiFi Names, feel free to share them with all of us through the comment section.
"First pay the fees of wifi", It's my suggestion. Hope you'll like it. I really like your collection.
its amazing how you pulled such a list. it must be really time consuming?


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Football, Football, And More Football – Cont'd 2
Home » Uncategorized » Football, Football, And More Football – Cont'd 2
I've seen that the discussion on whether we ought to have the option to remain in terraced regions at specific football grounds has come around once more. It generally appears to raise its head at some point among September and Christmas, when the fans are tired of plunking down and tired of being told to do as such by the stewards.
Presently let me explain the possibility that is believed to be the response - All Premiership and Coca-Cola Championship clubs ought to be permitted a limited quantity of terraced region for the fans, that wish to do as such, to stand and root for their group as in bygone times. You recall, scarves in the air, better reciting, better bedeviling and a superior environment.
I can surely see many motivations behind why Sports Minister Gerry Sutcliffe when asked, has as of late rejected the thought saying: "I have not heard anything to make me adjust my perspective"
He seriously loves the Taylor Report which came out four months later the Hillsborough Disaster.
The report gave numerous suggestions which have significantly added security to every one of our stadia, football grounds, yet music settings, athletic arenas and even music scenes. So there is no rejecting that a ton needed to change at our football grounds. I was one of the numerous Manchester United Fans that made the short excursion to the doors of Anfield to set out my scarf close by numerous other club's tones. You could contact the bitterness, it was substantial. เว็บบาคาร่า อันดับ1
Be that as it may, I think times change and we need to change with them.
I without a doubt can't see an issue with specific clubs having a confined measure of room to have a terraced region for the fans that wish to stand and support their group. To have the option to hop up when they score, to have the option to remain standing up when they are singing and, incidentally, have you at any point seen a show vocalist hypnotize you while they are plunking down? Or on the other hand more appropriately a hero belting it out, while slumped on a love seat? The climate is non existent now and again in a portion of the grounds.
The examples from Hillsborough were learnt well. Significant advances have been taken to guarantee our wellbeing. In any case, we HAVE learnt, to the degree that even the actual fans wouldn't allow a similar terrible misfortune to happen once more. The terraced regions would be for the lifelong fans who might unquestionably see the value in it, assuming that my experience is anything to continue.
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Ayurveda, a Sanskrit term meaning ' science of life', is the most ancient system of medicine in widespread practice today. In fact, the archeological findings of some Manuscripts support the notion that Ayurveda has been in continuous practice for more than 5000 years. Founded by God " Dhanvatari " a long era ago for the healthy living of people. Ayurveda is the most primitive treatment system of medicine.
Ayurveda traces its etymology to Ayush, meaning 'life', and veda, which originates from vid or knowledge.The practice of Ayurveda finds its roots in a body of knowledge and principles that were systematized in the Charaka Samhita , a treatise written more than two thousand years ago. Ayurveda, an ancient healing system and a medical science, first seeks to restore, and then maintain Svastha of the body and mind. Svastha, meaning health in English, has a definition in Ayurveda that, when fully understood, sheds much light on the objective of this system of medicine. Health is defined "as physical and mental well-being; freedom from disease, pain, or defect; normalcy of physical and mental functions; soundness."
Ayurveda, defines Svastha in Sanskrit as:
"sama dosah samagnis ca sama dhatu mala kriyah prasannatmendriya manah svastha ityabhihiyate"- Sushruta Samhita
One who is established in Self, who has balanced doshas, balanced agni, properly formed dhatus, proper elimination of malas, well functioning bodily processes, and whose mind, soul, and senses are full of bliss, is called a healthy person ."
In this Ayurvedic definition of health, not only is the western concept of health encompassed, but there are other layers, that of the doshas, agni, dhatus, and malas. Hence, Ayurveda views health as a balance of the doshas which are vata, pitta, kapha (in consideration of one's prakruti), properly formed/functioningdhatus (seven tissues of the body), and proper elimination of malas (waste products). Thus, when Ayurveda looks at a disease such as leucoderma, invariably, these aforementioned factors are taken into account and discussed. Unique to Ayurveda, is its understanding and articulation of disease.
In western medicine when a disease or a group of symptoms is examined, analyzed, then given a name, the disease has been 'diagnosed'. In contrast, the Ayurvedic approach to disease diagnosis seeks first to define its' root, or its' roganam mulakaranani; it is considered as important to define the root of a disease as it is to name a disease.
In Ayurvedic medicine, the process by which a disease is understood and diagnosed is called sarvaroga nidanam and is composed of five parts: nidanam(causative factors or etiology), purvarupa (earliest signs/symptoms), rupa (clinical signs/symptoms), samprapti (pathogenesis of the condition), and upasaya(diagnostic tests). Traditionally, when a disease is being discussed, the five parts of the sarvaroga nidanam will be presented.
Its holistic approach to a patient makes Ayurveda unique and highly effective in the treatment of most diseases, which are not cured by other system of medicines.
Unlike modern medicine, Ayurveda treats the individual and not merely the disease. It takes a holistic view of health, emphasizing a balanced diet; physical fitness, healthy lifestyle, hygiene and body care for disease free, long and healthy life. Basically herbs in form of powder, juices, pastes, and decoction are used as medicines but now Doctor's of Ayurveda also frequently uses herbal extracts for prompt and fast healing. The name and fame of Ayurveda is fast spreading to many countries across globe because of its neutrality, permanency in results, no side effects and uniqueness to deal with many diseases, which are not being treated by other system of medicines.


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Meet our cover artist: Olivia di Liberto
by Staff on December 1st, 2022 at 7:25 AM
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Hello! Tell us what you do.
I'm an artist, Illustrator, teacher and graphic designers. So I do a bit of everything, using similar styles throughout all of those mediums.
And how did you discover that style?
I started developing my illustrated style when I was in graphic design school. I had just moved to Vancouver, so I think it was very influenced by the West Coast. I continued to draw and do my illustrative work.
I was doing work for local bands – posters and album covers, that kind of thing – and was starting to be more recognized in the city. Once I started to paint, I applied to the Mural Festival. I got accepted and that's where I did my first one, on 3rd Ave, near Ontario. The organizers there are really good at guiding you, if you've never done one before.
I started getting opportunities after that. I started doing a lot of stuff like in that neighborhood and in the community. I did a label for Electric Bicycle Brewing. And yeah, more stuff for bands and stuff like that. Since I did that mural, people could see that I could apply it in different ways.
That's how we found you. What was your approach for this cover?
So I wanted it to reflect the West Coast. I wanted to include Vancouver's landscape and elements that make it unique, specifically East Van and downtown.
I also wanted to incorporate some of the past themes from past Straight covers, like from the Sixties – which is where the woman comes in – and bridge that with where the paper's at today and where you want it to go. So it's basically just a representation of the community.
I'm definitely trying to become a bit more international, not just working specifically in Vancouver. I'll be doing more Canada-wide projects, then hopefully, eventually, around the world. That would be my next step.
You can find more of Olivia's work on Instagram at @chillivia.


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\section{Introduction}
As computational power increases, multi-modality sensing has become more and more popular in recent years. Especially in the area of Autonomous Driving (AD), multiple sensors are combined to overcome the drawbacks of individual ones, which can provide redundancy for safety. Nowadays, most self-driving cars are equipped with lidar and cameras for both perception and mapping. Simulation systems have become essential to the development and validation of AD  technologies. Instead of using computer graphics to create virtual driving scenarios, Li et al.~\cite{Lieaaw0863} proposed to augment real-world pictures with simulated traffic flow to create photorealistic simulation images and renderings. One key component in their pipeline is to remove those moving agents on the road to generate clean background street images. AutoRemover~\cite{zhang2019autoremover} generated those kinds of data using the augmented platform and proposed a video inpainting method based on the deep learning techniques. Those map service companies, which display street-level panoramic views in their map Apps, also choose to place depth sensors in addition to image sensors on their capture vehicles. Due to privacy protection, those street view images have to be post-processed to blur human faces and vehicle license plates before posted for public access. There is a strong desire to totally remove those agents on the road for better privacy protection and more clear street images.

Significant progress has been made in image inpainting in recent years. The mainstream approaches~\cite{simakov2008summarizing, Darabi12:ImageMelding12, Efros:2001:IQT:383259.383296} adopt the patch-based method to complete missing regions by sampling and pasting similar patches from known regions or other source images. The method has been naturally extended to video inpainting, where not only spatial coherence but also temporal coherence are preserved.

The basic idea behind video inpainting is that the missing regions/pixels within a frame are observed in some other frames of the same video. Under this observation, some prior works~\cite{Xu_2019_CVPR, Huang-SigAsia-2016, wang2018videoinp} use optical flow as guidance to fill the missing pixels either explicitly or implicitly. They are successfully applied in different scenarios with seamless inpainting results. However, flow computation suffers from textureless areas, no matter it's learning based or not. Furthermore, perspective changes in the video could also degrade the quality of optical flow estimation. These frame-wise flow errors are accumulated when we fill missing pixels from a temporally distant frame, resulting in distorted inpainting results, which will be shown in the experiment section.

The emergence of deep learning, especially Generative Adversarial Networks (GAN), has provided us a powerful tool for inpainting. For images, \cite{IizukaSIGGRAPH2017, pathakCVPR16context, yu2018generative} formulate inpainting as
a conditional image generation problem. Although formulated differently, GAN based inpainting approaches are essentially the same as the patch-based approach, since the spirit is still looking for similar textures in the training data and fill the holes. Therefore, they have to delicately choose their training data to match the domain of the input images. And domain adaptation is not an easy task once the input images come from different scenarios. Moreover, GAN-based approaches share the same problem as the patch-based methods that they are poor at handling perspective changes in images.

As image+depth sensors become standard for AD cars, we propose a method to inpaint street-view videos with the guidance of depth. Depending on the tasks, target objects are either manually labeled or automatically detected in color images, and then removed from their depth counterpart. A 3D map is built by stitching all point clouds together and projected back onto individual frames. Most of the frame pixels are assigned with a depth value via 3D projection and those remaining pixels get their depth by interpolation. With a dense depth map and known extrinsic camera parameters, we are able to sample colors from other frames to fill holes within the current frame.  These colors serve as an initial guess for those missing pixels, followed by regularization enforcing spatial and photometric smoothness. After that, we apply color harmonization to make smooth and seamless blending boundaries. In the end, a moving average is applied along the optical flow to make the final inpainted video look smooth temporally.

Unlike learning-based methods, our approach can't inpaint occluded areas if they are never visible in the video. To solve this problem, we propose fusion inpainting, which makes use of multiple video clips to inpainting a target region. Compared to state-of-the-art inpainting approaches, we are able to preserve better details in the missing region with correct perspective distortion. Temporal coherence is implicitly enforced since the 3D map is consistent across all frames. We are even able to inpaint multiple video clips captured at different times by registering all the frames into a common 3D point map. Although our experiments are conducted on datasets captured from a self-driving car, the proposed method is not limited to this scenario only. It can be generalized to both indoor and outdoor scenarios, as long as we have synchronized image+depth data.

In this paper, we propose a novel video inpainting method with the guidance of 3D maps in AD scenarios. We avoid using deep learning-based methods so that our entire pipeline only runs on CPUs. This makes it easy to be generalized to different platforms and different use cases because it doesn't require GPUs and domain adaptation of training data. 3D map guided inpainting is a new direction for the inpainting community to explore, given that more and more videos are accompanied with depth data. The main contributions of this paper are listed as follows:

\begin{enumerate}
\item {
We propose a novel approach of depth guided video inpainting for autonomous driving;}

\item {We are the first to fuse multiple videos for inpainting, in order to solve the long time occlusion problem;}
\item {We collect a new dataset in the urban road with synchronized images and Lidar data including many challenge inpainting scenes such as long time occlusion;}

\item {Furthermore, we designed Candidate Color Sampling Criteria and Color Harmonization for inpainting. Our approach shows smaller RMSE compared with other state-of-art methods. }
\end{enumerate}

\section{Related Work}

The principle of inpainting is essentially filling the target holes by borrowing appearance information from known sources. The sources could be regions other than the hole in the same image, images from the same video or images/videos of similar scenarios. It's critical to reduce the search space for the right pixels. Following different cues, prior works can be categorized into 3 major classes: propagation-based inpainting, patch-based inpainting, and learning-based inpainting.

\textbf{Propogation-based Inpainting}. Propagation-based  methods~\cite{Ballester:2001:FJI:2318999.2320160, 2015ITIP243034E} extrapolate boundary pixels around the holes for image completion.  These approaches are successfully applied to regions of uniform colors. However, it has difficulties to fill large holes with rich texture variations. Thus, Propagation-based approaches usually repair small holes and scratches in an image.

\textbf{Patch-based Inpainting}. Patch-based methods~\cite{Bertalmio:2003:SST:2319023.2320434, Darabi12:ImageMelding12, Efros:2001:IQT:383259.383296, simakov2008summarizing} on the other hand, not only look at the boundary pixels but also search in the other regions/images for similar appearance in order to complete missing regions. This kind of approach has been extended to the temporal domain for video inpainting~\cite{Newson:2013:TFG:2534008.2534019, vics, Shih:2009:EVI:1641626.1641630}.   Huang et al.~\cite{Huang-SigAsia-2016} jointly estimate optical flow and color in the missing regions to address the temporal consistency problem. In general, patch-based methods can better handle non-stationary visual data. As suggested by its name, Patch-based methods depend on reliable pixel matches to copy and paste image patches to missing regions. When a pixel match can't be robustly obtained, for example in cases of big perspective changes or illumination changes, the inpainting results are problematic.

\textbf{Learning-based Inpainting}. The success of deep learning techniques inspires recent works on applying it for image inpainting.   Ren et al.~\cite{NIPS2015_5774} adds a few feature maps in the new Shepard layers, achieving stronger results than a much deeper network architecture. Generative Adversarial Networks(GAN~\cite{NIPS2014_5423}) was first introduced to generate novel photos. It's straightforward to extend it to inpainting by formulating inpainting as a conditional image generation problem~\cite{IizukaSIGGRAPH2017, pathakCVPR16context, yu2018generative}. Pathak et al.~\cite{pathakCVPR16context} proposed context encoders, which is a convolutional neural network trained to generate the contents of an arbitrary image region conditioned on its surroundings. The context encoders are trained to both understand the content of the entire image, and produce a plausible hypothesis for the missing parts. Iizuka et al.~\cite{IizukaSIGGRAPH2017} used global and local context discriminators to distinguish real images from fake ones. The global discriminator looks at the entire image to ensure it is coherent as a whole, while the local discriminator looks only at a small area centered at the completed region to ensure the local consistency of the generated patches. More recently, Yu et al.~\cite{yu2018generative} presented a contextual attention mechanism in a generative inpainting framework, which further improves the inpainting quality. For video inpainting, Xu et al.~\cite{Xu_2019_CVPR} formulated an effective framework that is specially designed to exploit redundant information across video frames. They first synthesize a spatially and temporally coherent optical flow field across video frames, then the synthesized flow field is used to guide the propagation of pixels to fill up the missing regions in the video.

\section{Proposed Approach}

\begin{figure*}[t!]
\centering
\includegraphics[width=1.0\linewidth]{images/pipeline1.png}
   \caption{Frame-wise point clouds (a) are stitched into a 3D map (b) using LOAM. The 3D map is projected onto a frame (c) to generate a depth map. For each pixel in the target region (e), we use its depth (d) as guidance to sample colors from other frames (f). Final pixel values are determined by BP regularization and color harmonization to ensure photometric consistency. (g) shows the final inpainting result.}
\label{fig:pipeline}
\end{figure*}

Fig.~\ref{fig:pipeline} shows a brief pipeline of our approach. A 3D map is first built by stitching all point clouds together, and projected back onto individual frames. With dense depth map and known extrinsic camera parameters, we are able to sample candidate colors from other frames to fill holes within current frame. Then, a belief propagation based regularization is applied to make sure pixel colors within the inpainting region are consistent with each other. It is followed by a color harmonization step which ensures that colors within inpainting region are consistent with outside regions. More details will be described in the following subsections.

\subsection{3D Depth Map}

\textbf{Dynamic Object Removal}. We first remove the moving objects from the point cloud, only keep the background points in the final 3D map. It is straight-forward to do so once the calibration between the depth sensor and the image sensor is performed. All points that are projected in the bounding box of the image are removed. The bounding boxes can be automatically detected or manually labeled. Alternatively, we can use PointNet++~\cite{qi2017pointnetplusplus} to detect and remove those typical moving objects directly from the point cloud.

\textbf{3D Map Stitching}. For lidar sensors, LOAM~\cite{Zhang-2014-7903} is a quite robust tool to fuse the multiple frames to build the 3D map. It is capable to match and track geometric features even with a sparse 16-beam lidar. For other dense depth sensors, such as Kinect, \cite{Izadi:2011:KRR:2047196.2047270} and \cite{6130321} proposed real-time solutions to reconstruct a 3D map which can be further down-sampled to generate the final point cloud with a reasonable resolution.

\textbf{Camera Pose Refinement}. The relative poses between depth sensor and image sensor can be calibrated in advance, but there are still some misalignments between the point cloud and image pixels, as shown in Fig.~\ref{fig:misalignment}. Vibrations, inaccurate synchronization, and accumulative errors from point cloud stitching cause pose offset between the image sensor and depth sensor.

\begin{figure}[t!]
\centering
\includegraphics[width=0.7\linewidth]{images/rcomp.png}
   \caption{The point cloud is projected into the target region (red box) with colors. The left image shows projection by calibration result. Obvious misalignment can be seen at boundaries. The right image shows projection by optimized rotation \textbf{R}, where points match much better with surrounding pixels. The region between yellow and red boxes is where we compare colors of projected 3D points and image pixels to optimize camera rotation matrix \textbf{R}.
}
\label{fig:misalignment}
\end{figure}

In order to produce seamless inpainting results, such offset should be compensated even if it's minor in most times. From the initial extrinsic calibration between the image sensor and depth sensor, we optimize their relative rotation R and translation T by minimizing the photometric projection error. The error is defined as:
\begin{equation}
    E = \sum_{p\in\Omega}| c(p) - c(q) |^2,
	\label{equ:termCor}
\end{equation}
where $p$ is a pixel projection from 3D map. $\Omega$ is an area surrounding the target inpainting region, which is illustrated in Fig.~\ref{fig:misalignment} as the region between red and yellow boxes. $q$ is original pixel in the image overlaid by $p$. The function $c(\cdot)$ returns the value of a pixel.

Note that the colors and locations of a pixel are discrete values, making the error function $E$ not continuous on $\mathbf{R}$ and $\mathbf{T}$. We can't solve the following equation directly using the standard solvers, such as Levenberg-Marquardt algorithm or Gauss-Newton algorithm. Instead, we search discrete spaces of $\mathbf{R}$ and $\mathbf{T}$ to minimize $E$. However, $\mathbf{R}$ and $\mathbf{T}$ have 6 degrees of freedom (DoF) in total, making the searching space extremely large. We choose to fix $\mathbf{T}$ and only optimize $\mathbf{R}$ because $\mathbf{R}$ is dominant at determining projection location when the majority of the 3D map are distant points. Moreover, in most cases, we only need to move projection pixels slightly in vertical and horizontal directions in the image space, which are determined by pitch and yaw angles of the camera. We finally reduce our search space to 2 DoF, which significantly speed up the optimization process.

\textbf{Depth Map}. Once the camera pose is refined, we project the 3D map onto each image frame to generate the corresponding depth map. Note that some point clouds are captured far from the current image, which can be occluded and de-occluded during the projection process. Hence, we employ z-buffer to get the nearest depth.

\begin{figure}[t!]
\centering
\includegraphics[width=0.35\linewidth]{images/dst.jpg}
\includegraphics[width=0.35\linewidth]{images/depth.jpg}
   \caption{A color image and its corresponding dense depth map. Note that the depth is only rendered for background points and all moving objects have been removed.}
\label{fig:depthmap}
\end{figure}

To get a fully dense depth map, we could definitely borrow some of the fancy algorithms (e.g. \cite{cheng2018depth}) that learn to produce dense depth maps from sparse ones. However, we find that the simple linear interpolation is good enough to generate the dense 3D map in our cases. We further apply a $5\times5$ median filter to remove some individual noise points. The final depth map is shown in Fig.~\ref{fig:depthmap}.

\subsection{Candidate Color Sampling Criteria}

As every pixel is assigned a depth value, it is possible to map a pixel from one image to other images. There are multiple choices of colors to fill in the pixels of the target inpainting region, a guideline should be followed to find the best candidate color. We have 2 principles to choose the best color candidate: 1) always choose from the frame that is closer to the current frame temporally and 2) always choose from the frame where the 3D background is closer to the camera. Please refer to Fig.~\ref{fig:colorsel} for an example of our candidate selection criteria. The first requirement ensures our inpainting approach suffers less from perspective distortion and occlusion. And the second requirement is because image records more texture details when it's closer to objects, so that we can retain more details in the inpainting regions.

\begin{figure}[t!]
\centering
\includegraphics[width=1.0\linewidth]{images/colorsel1.png}
   \caption{Color candidate selection criteria. Top row: a pixel finds its candidate colors in 2 later frames where road texture appears clear in both images. In this case, we choose the frame that is temporally close to the current frame, in order to minimize the impact of perspective change and potential occlusion or de-occlusion. Bottom row: a pixel finds its candidate colors in one previous frame and one later frame. In this case, we prefer the later frame over the previous one, since road texture is lost in the previous frame.}
\label{fig:colorsel}
\end{figure}

Under this guideline and the fact that sensors only move forwards during capture, our algorithm works by first searching forwards temporally to the end of video and then backwards until beginning. The first valid pixel is chosen as the candidate. And the valid pixel means its location doesn't fall into the target inpainting regions.

\subsection{Regularization with Belief Propagation}

At this point, every pixel gets color value individually. If the camera pose and depth value are 100\% correct, we can generate perfect inpainting results with smooth boundaries and neighbors. However, it's not the case in the real world, especially, the depth map always carries errors. Therefore, we have to explicitly enforce some smoothness constraints.

We formulate the color selection as a discrete global optimization problem and solve it using belief propagation (BP). Before explaining the formulation, we first define the color space and neighbors of a target pixel. As shown in left image pair in Fig.~\ref{fig:neighbors}, a target pixel (left red box) finds its candidate pixel (right red box) from a source image, due to depth inaccuracy, the true color might not lie exactly on the candidate pixel, but a small window around. So we collect all pixel colors from this small n by n window to form the color space for the target pixel. The right image pair in Fig.~\ref{fig:neighbors} illustrates how to find out the expected colors of neighbors. Because of perspective changes, the 4 neighbors of a target pixel are not necessarily neighbors in the source image. Hence, we warp neighbor pixels into the source image by their depth value to sample the expected colors.
\begin{figure}[t!]
\centering
\includegraphics[width=1.0\linewidth]{images/neighbors.png}
   \caption{Left image pair: potential color choices for a target pixel. a pixel within inpainting region find its candidate pixel (red box) from a source image. A small window of pixels around this candidate (yellow boxes) are all potential colors to fill the target pixel. Right image pair: the 4 neighbors of a target pixel are not necessarily neighbors in the source image due to perspective change. In order to get neighbor colors, we need to warp neighbor pixels into the source image by their depth value. }
\label{fig:neighbors}
\end{figure}

Let $P$ be the set of pixels in the target inpainting region and L be a set of labels. The labels correspond to the indices of potential colors in the color space. A labeling function l assigns a $l_p \in L$ to each pixel $p \in P$. We assume that the labels should vary smoothly almost everywhere but may change dramatically at some places such as object boundaries. The quality of labeling is given by an energy function as
\begin{equation}
E = \sum_{(p, q) \in N}V(l_p, l_q) + \sum_{p \in P}D_p(l_p),
\end{equation}
where $N$ are the number of edges in the four-connected image grid graph. $V(l_p, l_q)$ is the cost of assigning labels $l_p$ and $l_q$ to two neighboring pixels, and is normally referred to as the discontinuity cost. $D_p(l_p)$ is the cost of assigning label $l_p$ to pixel $p$,  which is referred to as the data cost. Determining a labeling with minimum energy corresponds to the Maximum A Posteriori (MAP) estimation problem.

We incorporate boundary smoothness constraint into the data cost as following:
\begin{equation}
D_p(l_p) =
\begin{cases}
|C_{pl}(l_p) - I(q)|, & \text{if p is left boundary pixel} \\
|C_{pr}(l_p) - I(q)|, & \text{if p is right boundary pixel} \\
|C_{pt}(l_p) - I(q)|, & \text{if p is top boundary pixel} \\
|C_{pb}(l_p) - I(q)|, & \text{if p is bottom boundary pixel} \\
\alpha, & \text{otherwise}
\end{cases} ,
\end{equation}
where $C_{pl}, C_{pr}, C_{pt}, C_{pb}$ return expected colors of pixel $p$'s left, right, top and bottom neighbors respectively. $q$ is the neighbor pixel of $p$ outside of the inpainting region in the target image, so it has known color, which is returned by the function $I(q)$. Here we take the difference of true neighbor color and expected neighbor color as a measure of labeling quality. For those pixels not on the inpainting boundary, we give equal opportunities to all the labels by assigning a constant value of $\alpha$. The discontinuity cost is defined as
\begin{equation}
V(l_p, l_q) =
\begin{cases}
|C_{pl}(l_p)-C_{q}(l_q)|+|C_p(l_p)-C_{qr}(l_q)| & \text{if L } \\
|C_{pr}(l_p)-C_{q}(l_q)|+|C_p(l_p)-C_{ql}(l_q)| & \text{if R } \\
|C_{pt}(l_p)-C_{q}(l_q)|+|C_p(l_p)-C_{qb}(l_q)| & \text{if T } \\
|C_{pb}(l_p)-C_{q}(l_q)|+|C_p(l_p)-C_{qt}(l_q)| & \text{if B}
\end{cases} .
\end{equation}
Here, $C_p(\cdot)$ and $C_q(\cdot)$ fetch colors for $p$ and $q$ at label $l_p$ and $l_q$. $L, R, T, B$ stand for $q$ is on left, on right, on top and on bottom respectively. For a pair of 2 neighboring pixels $p$ and $q$, we compute differences between $p$'s color and $q$'s expected color of $p$ and vice versa.

\begin{figure}[t!]
\centering
\includegraphics[width=1\linewidth]{images/colorharmo.png}
   \caption{Left image: input image. Middle image: inpainting result. Note the color discontinuity in yellow box and blank pixels in red box. Right image: result after color harmonization. }
\label{fig:colorharmo}
\end{figure}

\subsection{Color Harmonization}
Pixels from different frames may have different colors due to changing camera exposure time and white balance, causing color discontinuities (Fig.~\ref{fig:colorharmo}). We borrow Poisson image editing~\cite{Perez:2003:PIE:1201775.882269} to solve these problems. Poisson image editing is originally proposed to clone an image patch from source image into a destination image with seamless boundary and original texture. It achieve this by solving the following minimization problem
\begin{equation}
    \min_f{\int\int_\Omega|\Delta f-v|}  \text{   with   } f|_{\partial\Omega}=f^*|_{\partial\Omega}.
	\label{equ:poisson}
\end{equation}
Here all the notations are inherited from~\cite{Perez:2003:PIE:1201775.882269}. $\Omega$ is the inpainting region with boundary $\partial\Omega$. $f^*$ is color function of destination image and $f$ is color function of the target inpainting region within destination image. $\Delta.=[\partial./\partial x, \partial./\partial y]$ is gradient operator. $v$ is the desired color gradient defined over $\Omega$.

In our case, $v$ is computed using the output from the belief propagation step, with one exception. If two neighboring pixels within $\Omega$ are from different frames, we set their gradient to 0. This guarantee color consistency within the inpainting regions. The effectiveness of this solution is demonstrated in Fig.~\ref{fig:colorharmo}. Note that the blank-pixel region is also filled up. Since blank pixels have 0 gradient values, solving the Poisson equation on this part is equivalent to smooth color interpolation.

\subsection{Video Fusion}
Our algorithm has an implicit assumption that the inpainting regions must be visible in some other frames. Otherwise, some pixels will remain blank, as can be seen from Fig.~\ref{fig:colorharmo}. Learning-based methods can hallucinate inpainting colors from their training data. In contrast, our approach can't inpaint occluded areas if they are never visible in the video, leaving blank pixels.

For small areas of blank pixels, a smooth interpolation is sufficient to fill the hole. However, in some cases, a vehicle in front could block a wide field of view for the entire video duration, leaving big blank holes. A simple interpolation will not be capable of handling this problem. A better way to address this issue would be capturing another video of the same scene, where the occluded parts become visible. Fortunately, it is straightforward to register newly captured frames into an existing 3D map using LOAM~\cite{Zhang-2014-7903}. Once new frames are registered and merged into the existing 3D map, inpainting is performed exactly the same way. Some of our results of video fusion can be found in the next section as well as in supplemental materials.

\subsection{Temporal Smoothing}
Finally, we compute both forward and backward optical flows for all the result frames. For every pixel in the target inpainting areas, we trace it into neighboring frames using the optical flow and replace its original color with average of colors sampled from neighbor frames.
\section{Experiments and Results}

\begin{figure}[b!]
\begin{tabular}{@{}c@{}}
    \centering
    \raisebox{-.5\height} {
    \includegraphics[width=0.167\linewidth]{images/comp3/80482_init.jpg}\hspace{-0.06 cm}
    \includegraphics[width=0.167\linewidth]{images/comp3/80972_init.jpg}\hspace{-0.06 cm}
    \includegraphics[width=0.167\linewidth]{images/comp3/57493_init.jpg}\hspace{-0.06 cm}
    \includegraphics[width=0.167\linewidth]{images/comp3/81673_init.jpg}\hspace{-0.06 cm}
    \includegraphics[width=0.167\linewidth]{images/comp3/85892_init.jpg}
    } Inputs
\end{tabular}

\begin{tabular}{@{}c@{}}
    \centering
    \raisebox{-.5\height} {
    \includegraphics[width=0.167\linewidth]{images/comp3/80482_colorblending.jpg}\hspace{-0.06 cm}
    \includegraphics[width=0.167\linewidth]{images/comp3/80972_colorblending.jpg}\hspace{-0.06 cm}
    \includegraphics[width=0.167\linewidth]{images/comp3/57493_colorblending.jpg}\hspace{-0.06 cm}
    \includegraphics[width=0.167\linewidth]{images/comp3/81673_colorblending.jpg}\hspace{-0.06 cm}
    \includegraphics[width=0.167\linewidth]{images/comp3/85892_colorblending.jpg}
    }  Ours
\end{tabular}

\begin{tabular}{@{}c@{}}
    \centering
    \raisebox{-.5\height} {
    \includegraphics[width=0.167\linewidth]{images/comp3/gi_80482.png}\hspace{-0.06 cm}
    \includegraphics[width=0.167\linewidth]{images/comp3/gi_80972.png}\hspace{-0.06 cm}
    \includegraphics[width=0.167\linewidth]{images/comp3/gi_57493.png}\hspace{-0.06 cm}
    \includegraphics[width=0.167\linewidth]{images/comp3/gi_81673.png}\hspace{-0.06 cm}
    \includegraphics[width=0.167\linewidth]{images/comp3/gi_85892.png}
    } Yu~\cite{yu2018generative}
\end{tabular}

\begin{tabular}{@{}c@{}}
    \centering
    \raisebox{-.5\height} {
    \includegraphics[width=0.167\linewidth]{images/comp3/dfgv_80482.png}\hspace{-0.06 cm}
    \includegraphics[width=0.167\linewidth]{images/comp3/dfgv_80972.png}\hspace{-0.06 cm}
    \includegraphics[width=0.167\linewidth]{images/comp3/dfgv_57493.png}\hspace{-0.06 cm}
    \includegraphics[width=0.167\linewidth]{images/comp3/dfgv_81673.png}\hspace{-0.06 cm}
    \includegraphics[width=0.167\linewidth]{images/comp3/dfgv_85892.png}
    } Xu~\cite{Xu_2019_CVPR}
\end{tabular}

\begin{tabular}{@{}c@{}}
    \centering
    \raisebox{-.5\height} {
    \includegraphics[width=0.167\linewidth]{images/comp3/vcs_80482.png}\hspace{-0.06 cm}
    \includegraphics[width=0.167\linewidth]{images/comp3/vcs_80972.png}\hspace{-0.06 cm}
    \includegraphics[width=0.167\linewidth]{images/comp3/vcs_57493.png}\hspace{-0.06 cm}
    \includegraphics[width=0.167\linewidth]{images/comp3/vcs_81673.png}\hspace{-0.06 cm}
    \includegraphics[width=0.167\linewidth]{images/comp3/vcs_85892.png}
    } Huang~\cite{Huang-SigAsia-2016}
\end{tabular}

\caption{5 frames from different video clips are demonstrated to compare our results with others.}
\label{fig:comp2}
\end{figure}

To our best knowledge, all the public datasets (including  DAVIS Dataset~\cite{perazzi2016benchmark}) for video inpainting don't come with depth, which is a must for our algorithm. Autonomous driving dataset ApolloScape~\cite{ma2019trafficpredict} indeed have both camera images and point clouds, but it's not adopted by research community to evaluate video inpainting. Plus, its dataset was captured by a professional mapping Lidar RIEGL, which is not a typical setup for an autonomous driving car. Thus, we captured our own dataset and compare to previous works on our dataset.

\subsection{Inpainting Dataset}

We use an autonomous driving car to collect large-scale datasets in urban streets. The data is generated from a variety of sensors, including Hesai Pandora all in one sensor (40-beam LiDAR, 4 mono cameras covering 360 degrees, 1 forward-facing color camera), and a localization system working at 10 HZ. The LiDAR is synchronized with embedded frontal facing wide-angle color camera. We recorded a total of 5 hours length of RGB videos includes 100K synchronized $1280\times720$ images and point cloud. The dataset includes many challenging scenes e.g. background is occluded by large bus, shuttle or truck in the intersection and the front car is blocking the front view all the time. For those long time occlusion scenarios, the background is missing in the whole video sequence. We captured these difficult streets/intersections more than once, providing us the data for video fusion inpainting. Our new dataset will be published with the paper.

\subsection{Comparisons}

We qualitatively and quantitatively compare our results to three state-of-the-art works: two video inpainting approaches~\cite{Xu_2019_CVPR, Huang-SigAsia-2016} and one image inpainting approach~\cite{yu2018generative}. For those two deep learning-based approaches~\cite{Xu_2019_CVPR} and~\cite{yu2018generative}, we re-train their models on our dataset by randomly sampling missing regions on input frames to perform a fair comparison.

\textbf{Qualitative Comparison}. In Fig.~\ref{fig:comp2}, we compare our results with three other methods. It is clear that our method produces better results than others. Even though Huang~\cite{Huang-SigAsia-2016} got smooth inpainting results, almost all the texture details are missing in their results. As shown, Yu~\cite{yu2018generative} and Xu~\cite{Xu_2019_CVPR} sometimes fill totally messy texture in the target regions.

\begin{figure}[b!]
\centering
\includegraphics[width=0.95\linewidth]{images/perspective_distortion_comp.png}
   \caption{Top row: a patch from source image needs to be used to inpaint an occluded region in target image. Although there is significant perspective change from source to target images, our method produces geometrically and visually correct results. While other methods either fail to recover detailed texture or fail to place the texture in the right place.}
\label{fig:perspective_comp}
\end{figure}

Fig.~\ref{fig:perspective_comp} illustrates our capability to handle perspective change between source and target frames. Since our method is based on 3D geometry, perspective changes are inherently handled correctly. However, existing methods have a hard time overcoming this issue. They either fail to recover detailed texture or fail to place the texture in the right place.

\begin{table}[]
\centering
 {%
\begin{tabular}{ l c c c c }
\hline \hline
Methods & MAE             & RMSE            & PSNR            & SSIM            \\ \hline
Yu~\cite{yu2018generative}     & 10.961          & 16.848          & 20.821          & 0.850          \\
Xu~\cite{Xu_2019_CVPR}     & 7.569          & 12.932          & 19.220          & 0.594          \\
Huang~\cite{Huang-SigAsia-2016}  & 6.924          & 11.017          & 20.022            & 0.762          \\
Ours   & \textbf{6.135} & \textbf{9.633} & \textbf{21.631} & \textbf{0.895} \\ \hline \hline
\end{tabular}
}
\caption{Quantitative comparison with other methods, where the best results are highlighted in bold. To be clear, the values of ``MAE'' and ``RMSE'' are the lower the better while the values of ``PSNR'' and ``SSIM'' are the higher the better. }
\label{tab:comp}
\end{table}

\textbf{Quantitative Comparison}. To quantitatively compare our method with other methods, we manually labeled some background areas as the target inpainting regions and use them as the ground truth. We utilize four metrics for the evaluations: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Peak Signal to Noise Ratio (PSNR), and Structural Similarity Index (SSIM). Tab.~\ref{tab:comp} shows the evaluation results of the baseline methods and our method. Note that our method outperforms others on all four metrics. Our method reduce RMSE by 13\% compared to SOTA method.

\subsection{Ablation Study}

\textbf{Poisson Image Blending} Fig.~\ref{fig:fusion} shows the effectiveness of applying Poisson image blending. Visible seams are obvious at boundaries of pixels coming from different frames. This is because our capturing camera is working under auto exposure and auto white balance mode. A same object may have different color tones in different frames from the same video, not to mention videos captured on different days. Tab.~\ref{tab:colorblending} shows the quantitative results with and without Poisson color blending. It is clear that color blending indeed improves the results.

\begin{table}[]
\begin{minipage}[t]{.48\textwidth}
\centering
{
\begin{tabular}{ l c c c c }
\hline
  Strategies   & MAE     & RMSE            & PSNR            & SSIM            \\ \hline
no blending     & 9.410          & 17.484          & 21.783          & 0.911          \\
blending        &  \textbf{6.497}        &  \textbf{13.009}          &  \textbf{22.312}          &  \textbf{0.917}         \\ \hline
\end{tabular}
\caption{Ablation study on Poisson color blending, where the best results are highlighted in bold. To be clear, the values of ``MAE'' and ``RMSE'' are the lower the better while the values of ``PSNR'' and ``SSIM'' are the higher the better.}
\label{tab:colorblending}
}
\end{minipage}%
\hspace{0.4cm}%
\begin{minipage}[t]{.48\textwidth}

\centering
{
\begin{tabular}{ l c c c c }
\hline
 Strategies & MAE             & RMSE            & PSNR            & SSIM            \\ \hline
no fusion           & 10.427          & 14.967          & 20.941          & 0.879          \\
fusion              & \textbf{6.059}          &  \textbf{8.333}          &  \textbf{21.195}          &  \textbf{0.882}          \\ \hline
\end{tabular}
\caption{Ablation study on multiple video fusion, where the best results are highlighted in bold. To be clear, the values of ``MAE'' and ``RMSE'' are the lower the better while the values of ``PSNR'' and ``SSIM'' are the higher the better.}
\label{tab:fusion}
}
\end{minipage}
\end{table}

\begin{figure*}
\centering

    \begin{minipage}{\textwidth}
    \centering
    \includegraphics[width=0.22\linewidth]{images/VideoFusionEffect/simi_1.jpg}\hspace{-0.06 cm}
    \includegraphics[width=0.22\linewidth]{images/VideoFusionEffect/simi_2.jpg}\hspace{-0.06 cm}
    \includegraphics[width=0.22\linewidth]{images/VideoFusionEffect/simi_3.jpg}\hspace{-0.06 cm}
    \includegraphics[width=0.22\linewidth]{images/VideoFusionEffect/simi_4.jpg}
    \end{minipage}

    \begin{minipage}{\textwidth}
    \centering
    \includegraphics[width=0.22\linewidth]{images/VideoFusionEffect/input_81152.jpg}\hspace{-0.06 cm}
    \includegraphics[width=0.22\linewidth]{images/VideoFusionEffect/input_81462.jpg}\hspace{-0.06 cm}
    \includegraphics[width=0.22\linewidth]{images/VideoFusionEffect/input_81703.jpg}\hspace{-0.06 cm}
    \includegraphics[width=0.22\linewidth]{images/VideoFusionEffect/input_81972.jpg}
    \end{minipage}

    \begin{minipage}{\textwidth}
    \centering
    \includegraphics[width=0.22\linewidth]{images/VideoFusionEffect/nofusion_81152_colorblending.jpg}\hspace{-0.06 cm}
    \includegraphics[width=0.22\linewidth]{images/VideoFusionEffect/nofusion_81462_colorblending.jpg}\hspace{-0.06 cm}
    \includegraphics[width=0.22\linewidth]{images/VideoFusionEffect/nofusion_81703_colorblending.jpg}\hspace{-0.06 cm}
    \includegraphics[width=0.22\linewidth]{images/VideoFusionEffect/nofusion_81972_colorblending.jpg}
    \end{minipage}

    \begin{minipage}{\textwidth}
    \centering
    \includegraphics[width=0.22\linewidth]{images/VideoFusionEffect/fusion_blank_81152.jpg}\hspace{-0.06 cm}
    \includegraphics[width=0.22\linewidth]{images/VideoFusionEffect/fusion_blank_81462.jpg}\hspace{-0.06 cm}
    \includegraphics[width=0.22\linewidth]{images/VideoFusionEffect/fusion_blank_81703.jpg}\hspace{-0.06 cm}
    \includegraphics[width=0.22\linewidth]{images/VideoFusionEffect/fusion_blank_81972.jpg}
    \end{minipage}

    \begin{minipage}{\textwidth}
    \centering
    \includegraphics[width=0.22\linewidth]{images/VideoFusionEffect/fusion_81152_colorblending.jpg}\hspace{-0.06 cm}
    \includegraphics[width=0.22\linewidth]{images/VideoFusionEffect/fusion_81462_colorblending.jpg}\hspace{-0.06 cm}
    \includegraphics[width=0.22\linewidth]{images/VideoFusionEffect/fusion_81703_colorblending.jpg}\hspace{-0.06 cm}
    \includegraphics[width=0.22\linewidth]{images/VideoFusionEffect/fusion_81972_colorblending.jpg}
    \end{minipage}

\caption{\textbf{1st row}: frames from video 1; \textbf{2nd row}: frames from video 2 captured on a different day; \textbf{3rd row}: results after Poisson color blending using video 2 only; \textbf{4th row}: direct inpainting results by fusing both videos; \textbf{5th row}: results after Poision color blending using both videos.}
\label{fig:fusion}
\end{figure*}

\textbf{Video Fusion}. Fig.~\ref{fig:fusion} shows fusion of 2 videos. 1st row shows four frames from a video and the 2nd row shows 4 frames from another video captured on a different day at the same traffic intersection. Here, our goal is to inpaint those foreground objects in the 2nd video. 3rd row shows output using video 2 only, where exists large blank regions. That is because front vehicles keep blocking certain areas during the entire capture time. It is clear that Poisson image blending is not capable of completing large blank holes. 4th row shows BP output after we fuse the 1st video into the 2nd one, where the blank holes are all gone. 5th row shows the final results after color blending and optical flow temporal smoothing. Tab.~\ref{tab:fusion} demonstrates effectiveness of video fusion.

The fusion of multiple videos for inpainting demonstrates another advantage of our proposed approach. For those existing video inpainting works, they haven't address the issue of long-time occlusion, neither did they proposed to fuse multiple videos for inpainting purpose. Please checkout video demos here: \url{https://youtu.be/iOIxdQIzjQs} .

\section{Conclusion}
In this paper, we propose an automatic video inpainting algorithm that removes object from videos and synthesizes missing regions with the guidance of depth. It outperforms existing state-of-the-art inpainting methods on our inpainting dataset by preserving accurate texture details. The experiments indicate that our approach could  reconstruct cleaner and better background images, especially in the challenging scenarios with long time occlusion scenes. Furthermore, our method may be generalized to any videos as long as depth exists, in contrast to those deep learning-based approaches whose success heavily depend on comprehensiveness and resemblance of training dataset.

redpajama_set_name: RedPajamaArXiv


text:
Moon Handbook 1st edition calendar
Posted by Randall Wood on Saturday, October 7. 2006
When you've finished the book, the manuscript has been edited, and you're signing copies, it's easy to forget just how much work went into transforming your idea into a finished product.
Here's the calendar and timeline Joshua and I published back in 2002 when we were writing the first edition of Moon Handbooks Nicaragua. Who says being a published author is glamorous?
Calendar and Progress
January '98 - April '00
Josue and Randy serve Peace Corps Nicaragua with group Nica-15. Both deplore the obvious lack of a decent travel guide to Nicaragua; they decide to write it.
After abandoning the idea for nearly a year, both Randy and Josue, now on opposite ends of the continent, simultaneously and without communicating, start to work on the book and proposal. Randy draws up a rough outline and does some preliminary research. Josue edits them and supplies some additional information. The ball is rolling.
January '01 - March '01
Intensive work and research both by Randy in Nicaragua and Josue in NYC. They decide the scope of Avalon Travel Publishing's Moon Handbooks Series matches their vision for the book. The pitch letter, an analysis of the competition and supporting charts, resumes, writing samples, and references are assembled. The Managua and Condega chapters are written and extensively edited for use inclusion in our proposal.
April 24 '01
Avalon Publishing accepts our proposal for the first ever "Moon Handbooks: Nicaragua by Joshua Berman and Randy Wood." And there was much rejoicing.
May - July '01
Contract negotiations continue with Avalon. Josue is busy in Honduras and Israel with the American Jewish World Service. Randy is neck deep in the Hurricane Mitch program in Nicaragua. Much work goes into the background chapters and the Leon chapter.
We sign the contract!!
September 20 '01
We convince Avalon the manuscript will be ready this year. The book will now be published in fall of 2002. Our final submission deadline is moved from February 2003 to February 2002.
We begin working on the intro chapters - On the Road and Introduction. Josue is now in NY and Randy is still in Managua.
October 9 '01
Josue finally comes south to Nicaragua. Work begins in earnest. First chapter: "Leon and Chinandega" is due October 15th. Our Intro and "On the Road" chapters are lookin' good.
October 15 '01
Leon and Chinandega chapter submitted. Now we dig into Esteli, Somoto, Ocotal, Jalapa, and the rest of the north. Everything will be submitted by February 2002.
November 15 '01
Esteli, Somoto, and Ocotal have been researched and written. All the background and history for the Atlantic Coast have been done. Field research is now focusing on Granada, Masaya, and the South, and then the Atlantic Coast. We're doing lots of background info on Sandino, the canals, the language, health and safety concerns, and so on.
December 1 '01
We establish our war room in a 4th story Managua apt. Randy and Josue strongly deny all allegations that it is christened the "Pimp Tower."
December 20 '01
Rivas, Ometepe, and San Juan del Sur (the south) are complete.
Randy finishes up with the Hurricane Mitch program and loses his privileges for both the Blackhawk helicopters and the HUMVEE. All regions have been researched and written except Puerto Cabezas, Waspam, Jalapa, Boaco and Chontales.
January 16 '02
The home stretch. Boaco and Chontales have been completed and Randy has popped out to get Rama as well. Final edits are underway.
The final two weeks are marathon non-stop 16 hour writing and editing sessions, during which time Randy gets engaged and Josue gets gas.
The manuscript is delivered at 11:45 PM, consisting of 140 documents, 4.2 megabytes of text files, and who knows how many hours of writing. The authors celebrate by sleeping.
February 15 '02
Maps and graphics are due on February 28. We are producing and cataloging over 50 maps, all of which have to be 100 percent accurate, and more than 400 photos.
At Avalon, work has begun designing the cover and editing the manuscript. Edits will continue through the next few months.
We meet our final deadline. The next day, as we sweat out the previous night's celebratory rum, we move out of the Pimp Tower and return to our respective surreal existences.
Categories: Nicaragua
Leaving (and Missing) Nicaragua
Warm beans and wood smoke: Nicaragua's north country
Monday, October 12 2015
Shreds of Memory: Managua, 2003
El Remanso
Friday, May 17 2013
San Diego at Fifteen
Tuesday, May 7 2013
Amigos de Nicaragua
Tuesday, September 13 2011
Rama, the Head of the River


text:
Max Manus
The Norwegian historical crusader Max Manus — who lived from 1914 to 1996 — retains household name status among Scandinavians for his acts of daunting courage during the Second World War, but curiously, few non-historians outside of Northern Europe have heard of him. This adventure epic from co-directors Joachim Roenning and Espen Sandberg (Bandidas) represents the first mainstream effort to dramatize Manus' efforts in feature form with elaborate re-creations of his heroic exploits. The bulk of the action spans April 9, 1940 through May 8, 1945, or the period of Nazi Germany's Norwegian occupation. Despite an almost complete lack of formal education, Max (Aksel Hennie) grows completely outraged when Norway falls to Germany and permits the presence of an occupying puppet government within two months. In response, Manus bands together with some other indignant Norwegians and promptly forms a resistance outfit known as the "Rognes Organization" — a group dedicated to collecting weapons, disseminating anti-Nazi propaganda, and exuding a fighting spirit. Later, when Manus is captured by the Germans, he makes not one but two astonishing escapes from the clutches of the Nazis, with his life intact — demonstrating, hands-down, that he has no fear. Following a period of saboteur training in Scotland, Manus — incredibly — slips right back into Norway, and commandeers a mission to sink Nazi supply ships in an Oslo harbor nighttime raid; though successful, it draws nasty acts of vengeance from a psychopathic Gestapo leader Sigfried Fehmer (Ken Duken) — an event that turns Sigfried and Max into arch enemies with the bitterest hatred for one another. Manus' heroic efforts for the liberation of Norway continue undaunted, however. Thomas Nordseth-Tiller authored the screenplay, reportedly taking a slight degree of liberty with historical facts.


text:
Posted inNews, Police & Fire
Vandalism, graffiti continue in Oak Park and River Forest
Looting minor as some stores proactively board windows
by Stacey Sheridan June 2, 2020 Updated February 23, 2021
Vandalism, looting and rioting have occurred throughout the nation, accompanying mass protests following the death of George Floyd, an African American man who died under the knee of a now fired and charged white Minneapolis police officer. Oak Park and River Forest have not gone unscathed, with multiple properties damaged in both villages.
Two women smashed the front window of EcoKarat Jewelry, 131 N. Marion St. in Oak Park, and stole merchandise the afternoon of June 1 before fleeing in a car. The car caught fire during its attempted escape and ended up in a village parking enclave at Austin Boulevard and Randolph. A second vehicle also caught fire. Oak Park fire personnel put out the fire. However, the witness could not identify the woman driving as having participated in the burglary. A 27-year-old woman was charged with looting and burglary in connection with the jewelry store break in.
Also in Oak Park, Tobacco & Vape, 6415 North Ave., was burglarized at about 10 p.m., June 1 after a window was smashed. The clerk at Denny's, 711 N. Harlem Ave., was held at gunpoint and $150 was taken at 9:50 p.m., June 1.
Oak Park police detained two juveniles in connection with an attempted break in at CFSC Currency Exchange, 1147 Madison St., at about 3:20 a.m., June 2.
Eight businesses in Oak Park and one in River Forest were damaged overnight May 31, despite curfews in both villages imposed to limit vandalism and looting to local businesses. Neither Oak Park police nor River Forest police made any arrests for that day.
In River Forest, vandals hit the CVS located at 7929 North Ave.
"A couple of subjects broke the front doors, went inside and made off with a couple of bottles of liquor. No arrests, citations, or other damage has occurred," said River Forest Police Chief Jim O'Shea.
The previous night in River Forest, a group of people attempted to rob the 7-Eleven convenience store at 1140 N. Harlem Ave.
"We had an armed robbery crew that was active in the city and suburbs try to enter our 7-Eleven on Harlem," said O'Shea. "The clerk had locked the door earlier and the offenders cracked the glass on the door but did not make entry."
Half of the businesses in Oak Park damaged the night of May 31 are located on North Avenue, including PCS Metro cell phone store, 6319 North Ave.; A-1 Pawn Shop, 6151 North Ave.; PNC Bank, 6621 North Ave.; and Suds 2000 laundromat, 6045 North Ave.
Suds 2000 received an estimated $20,000 worth of damage, after the offenders broke into the business and damaged a vending machine, a washing machine card vending machine, as well as an ATM, which they unsuccessfully attempted to steal, according to police reports.
MedMen marijuana dispensary, 1132 Lake St.; Oak Park Jewelers, 101 S. Marion St.; Walgreens, 811 Madison St.; and Dream Town shoe store, 4 Madison St., are among other Oak Park businesses to have sustained damage May 31.
On the Chicago side of North Avenue, MY Beauty Supply, 6014 W. North Ave., and Pru/Utopia hair salon, 6118 W. North Ave., have windows boarded up.
The activity in Oak Park calmed around 1:30 a.m., June 1. As of that day, Oak Park police officers have made no arrests regarding the vandalism and break ins.
"Two suspects were detained in connection with Metro PCS incident, but the business owner declined to sign a complaint," said Oak Park spokesman David Powers. "A juvenile was detained in connection with the MedMen incident and turned over to parents. Police determined the juvenile did not cause the damage but had entered the business through a broken window."
Oak Park police officers are still in the process of determining the full extent of damages to many of these businesses. Much of the damage inflicted on Oak Park businesses include graffiti, as well as broken windows and doors.
According to a news release issued by the village of Oak Park, many of the reported graffiti incidents occurred in the 1000, 1100 and 1200 north blocks of Hayes, Lombard and Taylor avenues, as well as LeMoyne Parkway. Overhead garage doors and some vehicles within these blocks were graffitied.
Oak Park village government is devising a plan to work with the garage and vehicle owners to help remove the graffiti from their properties.
"Last night was an incredibly tough one for police," said Powers in a June 1 email to Wednesday Journal. "Tonight could be, too."
River Forest's O'Shea said, "Currently we are monitoring all intelligence information coming in from our county, state, and federal law enforcement partners, as well as, observing news media, social media, and regional police radio traffic," O'Shea said.
River Forest police officers are also actively patrolling residential and business areas within their jurisdiction.
"The River Forest Police Department maintains a high visibility presence within both the residential and business communities," said O'Shea. "The department has additional resources on hand to support keeping the village safe and viable."
One reply on "Vandalism, graffiti continue in Oak Park and River Forest"


City East College (RTO 90269) has funds available for subsidised training (conditions apply) . This training is subsidised by the NSW government. It is for people who have recognised 'barriers to learning' who would therefore have difficulty successfully completing a full qualification at say, TAFE or another private provider.
Training covered: The subsidy is available specifically for Nationally recognised training in part qualifications (maximum 50% of the qualification).
You will be required to complete an initial assessment form to see if you qualify.
Documentary evidence will be required to prove your eligibility as CEC is required to check eligibility, obtain evidence and retain it as proof that you are entitled to have your training subsidised by the NSW government.


text:
10 Game-Breaking Weapons, Spells & Abilities That Made Games Easy Early | OP Gear List
Kevin Thielenhaus / Features / Castlevania: Symphony of the Night, Dark Souls, Dead Rising, Deus Ex: Mankind Divided, Elder Scrolls IV: Oblivion, Fallout: New Vegas, Final Fantasy 7, Final Fantasy 8, Guides, Nioh, Persona 5, Resident Evil 4, Star Wars Knights of the Old Republic /
FINAL FANTASY VII REMAKE_20200412141746
Some games give you the tools to make your adventure incredibly easy right from the start. Whether it's a superior weapon, or a killer unlockable ability, some games just give you access to unbeatable tools early — and early is very relative. Some of these game-breaking items are obtainable almost instantly, others can be earned in the first dungeon. Either way, it's way before the end of your story, so you'll have plenty of time to go nuts.
Many of the most infamous weapons are from early games, but it's surprising just how many modern games also have incredibly powerful tools you can get early. Some abilities, like the Gourmet healing ability in Witcher 3, can be the very first skill you'll unlock — Gourmet gives you easy access to healing-over-time, but it's hardly a game-breaker. The following 10 games on our list are filled to the brim with boss-destroying tools of ultimate destruction.
No cheating or bug exploiting here; it's all intended! Even if it's completely OP.
#1: Black Knight Sword (Dark Souls)
Forget the Drake Sword — the Black Knight Sword is the best early weapon you can grab in Dark Souls. There's a 20% chance this useful sword will drop from the Black Knights found in some of the earliest areas of the game — and it's useful for the entire game. If you continue to upgrade it, the Black Knight Sword never lags behind, and you'll never need another weapon.
The Drake Sword is really good too; it just won't keep you going for the entire story. The Drake Sword can be acquired by knocking off the giant dragon's tail on the bridge in the early game — arrows are the safest way to do it, and the Drake Sword is a powerful crutch until you finally reach Sen's Fortress.
Image Source: [1]
#2: Enemy Skill Material (Final Fantasy 7)
Enemy Skill is an all-powerful materia you can acquire in Hojo's Lab, just a few hours into the epic story of FF7. With Enemy Skill equipped, your party can learn insane abilities from enemies just by absorbing their attacks — and if you learn the right abilities, you'll easily crush the rest of the campaign. Enemy Skill is infamously broken, and even more useful than ridiculous summons like Knights of The Round, which can only be obtained right before the end of the game.
The best skills to learn? Beta (from the Midgar Zolom snake), Aqualung (from Harpies / Serpents) and Trine (from the Godo / Silva bosses) are some of the best elemental attacks in the entire game. Even better, you can equip multiple Enemy Skill materia on the same character to earn skills, then give the materia to other characters to share the abilities with your party. With the right combos, you can become absurdly strong.
See how to break even more games on the next page of our list.


text:
Q: How to save ObservableCollection with custom XML tag? I've created an ObservableCollection of type string which contains loaded <Subject> strings from XML file. After adding few strings to Collection by TextBox I want to save updated state of that Collection to an XML file again. I would like each string to have a specific tag, just like this below:
<?xml version="1.0" encoding="utf-8"?>
<Subjects>
 <Subject>One</Subject>
 <Subject>Two</Subject>
 <Subject>Three</Subject>
</Subjects>

I would like to have <Subjects> root and each string in that Collection to have <Subject> child.
I've tried this:
namespace StudyNotes
{
    public class LocalData
    {
        public ObservableCollection<string> Subjects;

        public LocalData()
        {
            Subjects = new ObservableCollection<string>();
            LoadXmlData();
        }

        private void LoadXmlData()
        {
           XmlDocument xmlDocument = new XmlDocument();
           XmlNodeList xmlNodeList;
           using (FileStream fs = new FileStream(startupPath, FileMode.Open, FileAccess.Read))
           {
               xmlDocument.Load(fs);
               xmlNodeList = xmlDocument.GetElementsByTagName("Subject");
               foreach (XmlNode item in xmlNodeList)
               {
                   Subjects.Add(item.InnerText);
               }
               fs.Close();
            }
        }

        public void SaveXmlData()
        {
            Subjects.Add(TextBox.Text);

            var serializer = new XmlSerializer(typeof(ObservableCollection<string>));

            using (var stream = File.Open(xmlPath, FileMode.Create))
            {
                serializer.Serialize(stream, Subjects);
                stream.Close();
            }
        }
    }
}

Loading data from XML works well, it adds items to ObservableCollection from XML, but problem is in saving data. It doesn't create another node to XML with <Subject> tag, but <string> tag instead and unnecessary <ArrayOfString> tag instead of <Subjects>.
<?xml version="1.0"?>
<ArrayOfString xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema">
  <string>One</string>
  <string>Two</string>
  <string>Three</string>
</ArrayOfString>

What needs to be done, to add nodes/childs with specific tag from ObservableCollection to XML file?

redpajama_set_name: RedPajamaStackExchange