45篇，CVPR 2019 Best paper Finalist！最佳论文入围合集（含论文地址与代码链接）
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本文转载自极市平台
三大世界顶级计算机视觉会议之一「计算机视觉与模式识别会议」（Conference on Computer Vision and Pattern Recognition 2019，CVPR 2019）在美国长滩落下帷幕，顶会吸引全球超过9200位顶尖专家、学者以及产业界人士，共同推进 CV 技术的发展与落地。
1 “Probabilistic Permutation Synchronization using the RiemannianStructure of the Birkhoff Polytope,”
利用Birkhoff多面体的黎曼结构进行概率置换同步
作者：Tolga Birdal; UmutSimsekli
论文地址：https://arxiv.org/abs/1904.05814
2 “DeepC^3: Deep Instance Co-segmentation by Co-peak Search and Co-saliencyDetection,”
DeepCO 3：通过共峰搜索和共显性检测进行深度实例共同分割
作者：Kuang-JuiHsu; Yen-Yu Lin; Yung-Yu Chuang
代码链接：https://github.com/KuangJuiHsu/DeepCO3?
3 “SelFlow: Self-Supervised Learning ofOptical Flow,”
SelFlow：光流的自我监督学习
作者：PengpengLiu, Michael Lyu, Irwin King, Jia Xu
代码链接：https://github.com/ppliuboy/SelFlow
论文地址：https://arxiv.org/abs/1904.09117
4 “SDRSAC: Semidefinite-Based RandomizedApproach for Robust Point Cloud Registration Without Correspondences,”
SDRSAC：基于半定的随机方法，用于无通信的鲁棒点云注册
作者：HuuLe; Thanh-Toan Do; Tuan NA Hoang; Ngai-Man Cheung
代码链接：https://github.com/intellhave/SDRSAC
论文地址：https://arxiv.org/abs/1904.03483
5 “Deep Tree Learning for Zero-Shot FaceAnti-Spoofing,”
用于零镜头防欺骗的深度学习
作者：YaojieLiu; Joel Stehouwer; Amin Jourabloo; Xiaoming Liu
论文地址：https://arxiv.org/abs/1904.02860
6 “Neural RGB->D Sensing: Depth andUncertainty from a Video Camera,”
神经 RGB - > d 传感：深度和不确定性从一个摄像机
作者：Chao Liu; Jinwei Gu; KihwanKim; Srinivasa G Narasimhan; Jan Kautz
论文地址：https://arxiv.org/abs/1901.02571
7 “Self-Supervised 3D Hand Pose Estimation ThroughTraining by Fitting ,”
基于拟合训练的自监督三维手势估计
作者：Chengde Wan; Thomas Probst; Luc Van Gool; Angela Yao
代码链接：https://github.com/melonwan/sphereHand
8 “Neural Illumination: Lighting Predictionfor Indoor Environments,”
神经照明：室内环境的照明预测
作者：ShuranSong; Thomas Funkhouser
论文地址：https://arxiv.org/abs/1906.07370
9 “Shapes and Context: In-the-wild ImageSynthesis & Manipulation,”
形状和背景：野外图像合成和操作
作者：AayushBansal; Yaser Sheikh; Deva Ramanan
论文地址：https://arxiv.org/abs/1906.04728
10 “SiCloPe: Silhouette-based Clothed People,”
SiCloPe：基于剪影的穿着衣服的人
作者：RyotaNatsume; ShunsukeSaito; Zeng Huang; WeikaiChen; ChongyangMa;Shigeo Morishima; Hao Li
论文地址：https://arxiv.org/abs/1901.00049
11 “A General and Adaptive Robust LossFunction,”
一般自适应鲁棒损失函数
作者：Jonathan T Barron
代码地址：https://github.com/jonbarron/robust_loss_pytorch
论文地址：https://arxiv.org/abs/1701.03077
12 “2.5D Visual Sound,”
2.5D视觉声音
作者：RuohanGao; Kristen Grauman
代码链接：https://github.com/facebookresearch/FAIR-Play
论文链接：https://arxiv.org/abs/1812.04204
13 “Incremental Object Learning FromContiguous Views,”
从相邻视图进行增量对象学习
作者：Stefan Stojanov; Samarth Mishra; Ngoc Anh Thai; Nikhil Dhanda; AhmadHumayun; Linda Smith; Chen Yu; James Rehg
14 “Text2Scene: Generating Compositional Scenes from TextualDescriptions,”
Text2Scene：从文本描述生成合成场景
作者：FuwenTan; Song Feng; Vicente Ordonez
代码链接：https://github.com/uvavision/Text2Image%EF%BC%9F
论文地址：https://arxiv.org/abs/1809.01110
15 “Relational Action Forecasting,”
关系行动预测
作者：Chen Sun; Abhinav Shrivastava; Carl Vondrick; Rahul Sukthankar;Kevin Murphy; Cordelia Schmid
论文地址：https://arxiv.org/abs/1904.04231
16 “Shifting More Attention to Video SalientObject Detection,”
更加注重视频显着对象检测
作者：Deng-Ping Fan;WenguanWang; Ming-Ming Cheng; JianbingShen
代码链接：https://github.com/DengPingFan/DAVSOD
17 “GA-Net:Guided Aggregation Net for End-To-End Stereo Matching,”
GA-Net：用于端到端立体匹配的引导聚合网络
作者：FeihuZhang; Victor Prisacariu; Yang Ruigang; Philip Torr
代码链接： https://github.com/feihuzhang/GANet
论文地址：https://arxiv.org/abs/1904.06587
18 “ASkeleton-bridged Deep Learning Approach for Generating Meshes of ComplexTopologies from Single RGB Images,”
一种用于从单个RGB图像生成复杂拓扑网格的骨架桥接深度学习方法
作者：JiapengTang; Xiaoguang Han; JunyiPan; KuiJia; Xin Tong
论文地址：https://arxiv.org/abs/1903.04704
19 “Semantic Image Synthesis withSpatially-Adaptive Normalization,”
空间自适应归一化的语义图像合成
作者：TaesungPark; Ming-Yu Liu; Ting-Chun Wang; Jun-Yan Zhu
链接地址：https://github.com/NVlabs/SPADE
论文地址：https://arxiv.org/abs/1903.07291
20 “ContactDB: Analyzing and Predicting GraspContact via Thermal Imaging,”
ContactDB：通过热成像分析和预测抓取接触
作者：Samarth Brahmbhatt; CusuhHam; Charlie Kemp; James Hays
代码链接：https://github.com/samarth-robo/contactdb_prediction
论文地址：https://arxiv.org/abs/1904.06830
21 “RevealingScenes by Inverting Structure from Motion Reconstructions,”
通过从运动重建中反转结构来揭示场景
作者：Francesco Pittaluga; Sanjeev J Koppal; Sing Bing Kang; Sudipta Sinha
论文地址：https://arxiv.org/abs/1904.03303
22 “A Theory of Fermat Paths for Non-Line-Of-SightShape Reconstruction,”
非视线形状重建的费马路径理论
作者：ShumianXin; Sotiris Nousias; Kyros Kutulakos; AswinSankaranarayanan;Srinivasa G Narasimhan; IoannisGkioulekas
论文地址：https://www.ri.cmu.edu/publications/a-theory-of-fermat-paths-for-non-line-of-sight-shape-reconstruction/
23 “Relation-Shape Convolutional NeuralNetwork for Point Cloud Analysis,”
用于点云分析的关系形状卷积神经网络
作者：YongchengLiu; Bin Fan; ShimingXiang; ChunhongPan
代码链接：https://github.com/Yochengliu/Relation-Shape-CNN
论文地址：https://arxiv.org/abs/1904.07601
微信解读：https://mp.weixin.qq.com/s/KuoHFPpUYHPFacsBWZWVSg
24 “BubbleNets:Learning to Select the Guidance Frame in Video Object Segmentation by DeepSorting Frames,”
BubbleNets：学习通过深度排序帧在视频对象分割中选择指导框架
作者：Brent Griffin; Jason J Corso
代码链接：https://github.com/griffbr/BubbleNets
论文地址：https://arxiv.org/abs/1903.11779
25 “ImageDeformation Meta-Networks for One-Shot Learning,”
用于一次性学习的图像变形元网络
作者：ZitianChen; Yanwei Fu; Yu-Xiong Wang; Lin Ma; Wei Liu; MartialHebert
论文地址：https://arxiv.org/abs/1905.11641
26 “Estimating 3D Motion and Forces of Person-ObjectInteractions from Monocular Video,”
从单目视频估计人体 - 物体相互作用的三维运动和力
作者：ZongmianLi; Jiri Sedlar; Justin Carpentier; Ivan Laptev; NicolasMansard; Josef Sivic
论文地址：https://arxiv.org/abs/1904.02683
27“A Style-Based Generator Architecture forGenerative Adversarial Networks,”
基于样式的生成对抗网络生成器体系结构
作者：Tero Karras; SamuliLaine; Timo Aila
代码链接：https://github.com/NVlabs/stylegan
论文地址：https://arxiv.org/abs/1812.04948
28 “Unsupervised Part-Based Disentangling ofObject Shape and Appearance,”
基于无监督部分的物体形状和外观解缠结
作者：Dominik Lorenz; Leonard Bereska; Timo Milbich; Bjorn Ommer
论文地址：https://arxiv.org/abs/1903.06946
29 “Pushing the Boundaries of ViewExtrapolation with Multiplane Images,”
用多平面图像推动视图外推边界
作者：Pratul Srinivasan; Richard Tucker; Jonathan T Barron; RaviRamamoorthi; Ren Ng; Noah Snavely
论文地址：https://arxiv.org/abs/1905.00413
30 “Path-Invariant Map Networks,”
路径不变映射网络
作者：ZaiweiZhang; ZhenxiaoLiang; LemengWu; Xiaowei Zhou; Qixing Huang
代码链接：https://github.com/zaiweizhang/path_invariance_map_network
论文地址：https://arxiv.org/abs/1812.11647
31 “Learning the Depths of Moving People byWatching Frozen People,”
通过观察冰冻人物来学习移动人的深度
作者：ZhengqiLi; Tali Dekel; Forrester Cole; Richard Tucker; Ce Liu; BillFreeman; Noah Snavely
论文地址：https://arxiv.org/abs/1904.11111
32 “Efficient Online Multi-Person 2D PoseTracking with Recurrent Spatio-Temporal Affinity Fields,”
具有循环时空亲和场的高效在线多人2D姿态跟踪
作者：YaadhavRaaj; Haroon Idrees; GinesHidalgo Martinez; Yaser Sheikh
论文地址：https://arxiv.org/abs/1811.11975
33 “Learning to Compose Dynamic TreeStructures for Visual Contexts,”
学习为视觉上下文构建动态树结构
作者：KaihuaTang; HanwangZhang; BaoyuanWu; WenhanLuo; Wei Liu
代码链接：https://github.com/KaihuaTang/VCTree-Visual-Question-Answering
论文地址：https://arxiv.org/abs/1812.01880
34 “Cascaded Projection: End-to-End NetworkCompression and Acceleration,”
级联投影：端到端网络压缩和加速
作者：Breton L Minnehan; Andreas Savakis
论文地址：https://arxiv.org/abs/1903.04988
35“Taking a Deeper Look at the InverseCompositional Algorithm,”
深入研究逆组合算法
作者：ZhaoyangLv; Frank Dellaert; James Rehg; Andreas Geiger
代码链接：https://github.com/lvzhaoyang/DeeperInverseCompositionalAlgorithm
论文地址：https://arxiv.org/abs/1812.06861
36 “Occupancy Networks: Learning 3D Reconstruction in Function Space,”
占用网络：学习功能空间中的三维重建
作者：Lars M Mescheder; Michael Oechsle; Michael Niemeyer; SebastianNowozin (Google AI Berlin); Andreas Geiger
代码链接：https://github.com/autonomousvision/occupancy_networks
论文地址：https://arxiv.org/abs/1812.03828
37 “Geometry-Consistent Generative AdversarialNetworks for One-Sided Unsupervised Domain Mapping,”
单边无监督域映射的几何一致生成对抗网络
作者：Huan Fu; MingmingGong; ChaohuiWang; Kayhan Batmanghelich; Kun Zhang;DachengTao
论文地址：https://arxiv.org/abs/1809.05852
38 “Convolutional Mesh Regression forSingle-Image Human Shape Reconstruction,”
单像人体形状重建的卷积网格回归
作者：Nikos Kolotouros; Georgios Pavlakos; Kostas Daniilidis
代码链接：https://github.com/nkolot/GraphCMR
论文地址：https://arxiv.org/abs/1905.03244
39 “Neural Rerendering in the Wild,”
野外神经再造
作者：Moustafa Meshry; Ricardo Martin-Brualla; Noah Snavely; Hugues Hoppe;SamehKhamis; Rohit Pandey; Dan B Goldman
代码链接：https://github.com/google/neural_rerendering_in_the_wild
论文地址：https://arxiv.org/abs/1904.04290
40 “Content Authentication for Neural ImagingPipelines: End-to-end Optimization of Photo Provenance in Complex DistributionChannels,”
神经成像管道的内容认证：复杂分销渠道中照片来源的端到端优化
作者：Pawel Korus; Nasir Memon
代码链接：https://github.com/pkorus/neural-imaging
论文地址：https://arxiv.org/abs/1812.01516
41 “Reinforced Cross-Modal Matching andSelf-Supervised Imitation Learning for Vision-Language Navigation,”
视觉语言导航的强化跨模态匹配和自监督模拟学习
作者：Xin Wang; QiuyuanHuang; AsliCelikyilmaz; JianfengGao; DinghanShen;Yuan-Fang Wang; William Yang Wang; Lei Zhang
论文地址：https://arxiv.org/abs/1811.10092
微信解读：https://mp.weixin.qq.com/s/LsHWkdwqqrOPFgCNNcBdpg
42 “FilterReg: Robust and EfficientProbabilistic Point-Set Registration using Gaussian Filter and TwistParameterization,”
FilterReg：使用高斯滤波器和扭曲参数化的鲁棒且高效的概率点集注册
作者：Wei Gao; Russ Tedrake
代码链接：https://bitbucket.org/gaowei19951004/poser/src/master/
论文地址：https://arxiv.org/abs/1811.10136
43 “Locating Objects Without Bounding Boxes,”
定位没有边框的对象
作者：Javier Ribera; David Güera; Yuhao Chen; Edward Delp
代码链接：https://github.com/javiribera/locating-objects-without-bboxes
论文地址：https://arxiv.org/abs/1806.07564
44 “DeepSDF: Learning Continuous SignedDistance Functions for Shape Representation,”
DeepSDF：学习形状表示的连续符号距离函数
作者：Jeong Joon Park; Peter R Florence; Julian Straub; Richard Newcombe;Steven Lovegrove
代码链接：https://github.com/Oktosha/DeepSDF-explained?
论文地址：https://arxiv.org/abs/1901.05103
45 “CollaGAN: Collaborative GAN for MissingImage Data Imputation,”
CollaGAN：缺少图像数据插补的协作GAN
作者：DongwookLee; Junyoung Kim; Won-Jin Moon; Jong Chul Ye
论文地址：https://arxiv.org/abs/1901.09764
最后附上CVPR2019/CVPR2018/CVPR2017相关论文，代码，解读资料开源项目: https://github.com/extreme-assistant/cvpr2019, 欢迎Star~
-完-
觉得有用麻烦给个在看啦~ 