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Rapid advances in 2D perception have led to systems that accurately detect objects in real-world images.
Learning to Reconstruct Shapes from Unseen Classes
Xiuming Zhang, Zhoutong Zhang, Chengkai Zhang, Joshua B. Tenenbaum, William T. Freeman, and Jiajun Wu · 1906
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Marching cubes: A high resolution 3d surface construction algorithm
William E. Lorensen and Harvey E. Cline · 1987
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Wordnet: A lexical database for english
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A morphable model for the synthesis of 3d faces
Volker Blanz and Thomas Vetter · 1999
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Implicit fairing of irregular meshes using diffusion and curvature flow
Mathieu Desbrun, Mark Meyer, Peter Schröder, and Alan H. Barr · 1999
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A taxonomy and evaluation of dense two-frame stereo correspondence algorithms
Daniel Scharstein and Richard Szeliski · 2002
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Multiple view geometry in computer vision
Richard Hartley and Andrew Zisserman · 2003
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Geometric context from a single image
Derek Hoiem, Alexei A. Efros, and Martial Hebert · 2005
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3d object detection and viewpoint estimation with a deformable 3d cuboid model
Sanja Fidler, Sven Dickinson, and Raquel Urtasun · 2012
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ImageNet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoff Hinton · 2012
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Dense object reconstruction with semantic priors
Sid Yingze Bao, Manmohan Chandraker, Yuanqing Lin, and Silvio Savarese · 2013
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Dense reconstruction using 3d object shape priors
Amaury Dame, Victor A. Prisacariu, Carl Y. Ren, and Ian Reid · 2013
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Data-driven 3D primitives for single image understanding
David F. Fouhey, Abhinav Gupta, and Martial Hebert · 2013
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Vision meets robotics: The kitti dataset
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun · 2013
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Parsing IKEA Objects: Fine Pose Estimation
Joseph J. Lim, Hamed Pirsiavash, and Antonio Torralba · 2013
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Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
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Learning rich features from rgb-d images for object detection and segmentatio
Saurabh Gupta, Ross Girshick, Pablo Arbeláez, and Jitendra Malik · 2014
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Class specific 3d object shape priors using surface normals
Christian Häne, Nikolay Savinov, and Marc Pollefeys · 2014
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
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Microsoft COCO: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
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Deeppose: Human pose estimation via deep neural networks
Alexander Toshev and Christian Szegedy · 2014
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Beyond pascal: A benchmark for 3d object detection in the wild
Yu Xiang, Roozbeh Mottaghi, and Silvio Savarese · 2014
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Shapenet: An information-rich 3d model repository
Angel X. Chang, Thomas A. Funkhouser, Leonidas J. Guibas, Pat Hanrahan, Qi-Xing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu · 2015
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Fast R-CNN
Ross Girshick · 2015
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Spatial transformer networks
Max Jaderberg, Karen Simonyan, and Andrew Zisserman · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Faster R-CNN: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Completing 3d object shape from one depth image
Jason Rock, Tanmay Gupta, Justin Thorsen, JunYoung Gwak, Daeyun Shin, and Derek Hoiem · 2015
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Octnet: Learning deep 3d representations at high resolutions
Gernot Riegler, Ali Osman Ulusoy, and Andreas Geiger · 2017
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Self-supervised visual descriptor learning for dense correspondence
Tanner Schmidt, Richard Newcombe, and Dieter Fox · 2017
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Octree generating networks: Efficient convolutional architectures for high-resolution 3d outputs
Maxim Tatarchenko, Alexey Dosovitskiy, and Thomas Brox · 2017
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Learning shape abstractions by assembling volumetric primitives
Shubham Tulsiani, Hao Su, Leonidas J. Guibas, Alexei A. Efros, and Jitendra Malik · 2017
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Multi-view supervision for single-view reconstruction via differentiable ray consistency
Shubham Tulsiani, Tinghui Zhou, Alexei A. Efros, and Jitendra Malik · 2017
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Marrnet: 3d shape reconstruction via 2.5 d sketches
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Viewpoints and keypoints
Shubham Tulsiani and Jitendra Malik · 2015
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3D-R2N2: A unified approach for single and multi-view 3d object reconstruction
Christopher B. Choy, Danfei Xu, JunYoung Gwak, Kevin Chen, and Silvio Savarese · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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SSD: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, and Scott Reed · 2016
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You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi · 2016
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Unsupervised learning of 3d structure from images
Danilo Jimenez Rezende, S.M. Ali Eslami, Shakir Mohamed, Peter Battaglia, Max Jaderberg, and Nicolas Heess · 2016
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Jiajun Wu, Yifan Wang, Tianfan Xue, Xingyuan Sun, Bill Freeman, and Josh Tenenbaum · 2017
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3d semantic segmentation with submanifold sparse convolutional networks
Benjamin Graham, Martin Engelcke, and Laurens van der Maaten · 2018
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A papier-mâché approach to learning 3d surface generation
Thibault Groueix, Matthew Fisher, Vladimir G Kim, Bryan C Russell, and Mathieu Aubry · 2018
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Learning category-specific mesh reconstruction from image collections
Angjoo Kanazawa, Shubham Tulsiani, Alexei A. Efros, and Jitendra Malik · 2018
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Neural 3D mesh renderer
Hiroharu Kato, Yoshitaka Ushiku, and Tatsuya Harada · 2018
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3d-rcnn: Instance-level 3d object reconstruction via render-and-compare
Abhijit Kundu, Yin Li, and James M. Rehg · 2018
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Pointcnn: Convolution on x-transformed points
Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, and Baoquan Chen · 2018
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Learning efficient point cloud generation for dense 3d object reconstruction
Chen-Hsuan Lin, Chen Kong, and Simon Lucey · 2018
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3d-lmnet: Latent embedding matching for accurate and diverse 3d point cloud reconstruction from a single image
Priyanka Mandikal, Navaneet Murthy, Mayank Agarwal, and R. Venkatesh Babu · 2018
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Matryoshka networks: Predicting 3d geometry via nested shape layers
Stephan R. Richter and Stefan Roth · 2018
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Multi-view silhouette and depth decomposition for high resolution 3d object representation
Edward Smith, Scott Fujimoto, and David Meger · 2018
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SPLATNet: Sparse lattice networks for point cloud processing
Hang Su, Varun Jampani, Deqing Sun, Subhransu Maji, Evangelos Kalogerakis, Ming-Hsuan Yang, and Jan Kautz · 2018
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Pix3d: Dataset and methods for single-image 3d shape modeling
Xingyuan Sun, Jiajun Wu, Xiuming Zhang, Zhoutong Zhang, Chengkai Zhang, Tianfan Xue, Joshua B. Tenenbaum, and William T. Freeman · 2018
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Tangent convolutions for dense prediction in 3d
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, and Qian-Yi Zhou · 2018
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Pixel2Mesh: Generating 3D mesh models from single RGB images
Nanyang Wang, Yinda Zhang, Zhuwen Li, Yanwei Fu, Wei Liu, and Yu-Gang Jiang · 2018
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Adaptive O-CNN: a patch-based deep representation of 3d shapes
Peng-Shuai Wang, Chun-Yu Sun, Yang Liu, and Xin Tong · 2018
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Learning 3D Shape Priors for Shape Completion and Reconstruction
Jiajun Wu, Chengkai Zhang, Xiuming Zhang, Zhoutong Zhang, William T. Freeman, and Joshua B. Tenenbaum · 2018
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Occupancy networks: Learning 3d reconstruction in function space
Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger · 2019
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GEOMetrics: Exploiting geometric structure for graph-encoded objects
Edward J. Smith, Scott Fujimoto, Adriana Romero, and David Meger · 2019
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Learning to infer and execute 3d shape programs
Yonglong Tian, Andrew Luo, Xingyuan Sun, Kevin Ellis, William T. Freeman, Joshua B. Tenenbaum, and Jiajun Wu · 2019
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