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Reconstructing high-quality 3D objects from sparse, partial observations from a single view is of crucial importance for various applications in computer vision, robotics, and graphics.
Marching cubes: A high resolution 3d surface construction algorithm
William E. Lorensen and Harvey E. Cline · 1987
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Generalized implicit functions for computer graphics
Stan Sclaroff and Alex Pentland · 1991
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Are we ready for autonomous driving? the kitti vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
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Shapenet: An information-rich 3d model repository, 2015
Angel X. Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Category-specific object reconstruction from a single image
Abhishek Kar, Shubham Tulsiani, Joao Carreira, and Jitendra Malik · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Joint Object Pose Estimation and Shape Reconstruction in Urban Street Scenes Using 3D Shape Priors
Francis Engelmann, Jörg Stückler, and Bastian Leibe · 2016
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Matterport3d: Learning from rgb-d data in indoor environments, 2017
Angel Chang, Angela Dai, Thomas Funkhouser, Maciej Halber, Matthias Niessner, Manolis Savva, Shuran Song, Andy Zeng, and Yinda Zhang · 2017
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Scannet: Richly-annotated 3d reconstructions of indoor scenes
Angela Dai, Angel X Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner · 2017
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Shape completion using 3d-encoder-predictor cnns and shape synthesis
Angela Dai, Charles Ruizhongtai Qi, and Matthias Nießner · 2017
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Samp: Shape and motion priors for 4d vehicle reconstruction
Francis Engelmann, Jorg Stuckler, and Bastian Leibe · 2017
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High-resolution shape completion using deep neural networks for global structure and local geometry inference
Xiaoguang Han, Zhen Li, Haibin Huang, Evangelos Kalogerakis, and Yizhou Yu · 2017
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Colored point cloud registration revisited
J. Park, Q. Zhou, and V. Koltun · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas · 2017
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Octnet: Learning deep 3d representations at high resolutions
Gernot Riegler, Ali Osman Ulusoy, and Andreas Geiger · 2017
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Renderpeople, 2018
2018
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Codeslam - learning a compact, optimisable representation for dense visual slam
Michael Bloesch, Jan Czarnowski, Ronald Clark, Stefan Leutenegger, and Andrew J. Davison · 2018
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Ps-fcn: A flexible learning framework for photometric stereo
Guanying Chen, Kai Han, and Kwan-Yee K Wong · 2018
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Atlasnet: 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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3d-rcnn: Instance-level 3d object reconstruction via render-and-compare
Abhijit Kundu, Yin Li, and James M. Rehg · 2018
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Learning 3d shape completion under weak supervision
David Stutz and Andreas Geiger · 2018
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Foldingnet: Point cloud auto-encoder via deep grid deformation
Yaoqing Yang, Chen Feng, Yiru Shen, and Dong Tian · 2018
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Pcn: Point completion network
Wentao Yuan, Tejas Khot, David Held, Christoph Mertz, and Martial Hebert · 2018
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Efficient convolutions for real-time semantic segmentation of 3d point clouds
C. Zhang, W. Luo, and R. Urtasun · 2018
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Sdfdiff: Differentiable rendering of signed distance fields for 3d shape optimization
Yue Jiang, Dantong Ji, Zhizhong Han, and Matthias Zwicker · 2020
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Sdf-srn: Learning signed distance 3d object reconstruction from static images
Chen-Hsuan Lin, Chaoyang Wang, and Simon Lucey · 2020
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Morphing and sampling network for dense point cloud completion
Minghua Liu, Lu Sheng, Sheng Yang, Jing Shao, and Shi-Min Hu · 2020
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Dist: Rendering deep implicit signed distance function with differentiable sphere tracing
Shaohui Liu, Yinda Zhang, Songyou Peng, Boxin Shi, Marc Pollefeys, and Zhaopeng Cui · 2020
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Nerf: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng · 2020
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Learning implicit fields for generative shape modeling
Zhiqin Chen and Hao Zhang · 2019
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Scan2mesh: From unstructured range scans to 3d meshes
Angela Dai and Matthias Nießner · 2019
Cited alongside, same era.
Mesh r-cnn
Georgia Gkioxari, Jitendra Malik, and Justin Johnson · 2019
Cited alongside, same era.
High fidelity semantic shape completion for point clouds using latent optimization
Swaminathan Gurumurthy and Shubham Agrawal · 2019
Cited alongside, same era.
Occupancy networks: Learning 3d reconstruction in function space
Lars M. Mescheder, Michael Oechsle, M. Niemeyer, Sebastian Nowozin, and Andreas Geiger · 2019
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Deepsdf: Learning continuous signed distance functions for shape representation
Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove · 2019
Cited alongside, same era.
Mahyar Najibi, Guangda Lai, Abhijit Kundu, Zhichao Lu, Vivek Rathod, Thomas Funkhouser, Caroline Pantofaru, David Ross, Larry S. Davis, and Alireza Fathi · 2020
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Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision
Michael Niemeyer, Lars Mescheder, Michael Oechsle, and Andreas Geiger · 2020
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Convolutional occupancy networks
Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger · 2020
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Pifuhd: Multi-level pixel-aligned implicit function for high-resolution 3d human digitization
Shunsuke Saito, Tomas Simon, Jason Saragih, and Hanbyul Joo · 2020
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Interfacegan: Interpreting the disentangled face representation learned by gans
Yujun Shen, Ceyuan Yang, Xiaoou Tang, and Bolei Zhou · 2020
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Metasdf: Meta-learning signed distance functions
Vincent Sitzmann, Eric R. Chan, Richard Tucker, Noah Snavely, and Gordon Wetzstein · 2020
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Grf: Learning a general radiance field for 3d scene representation and rendering, 2020
Alex Trevithick and Bo Yang · 2020
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Directshape: Direct photometric alignment of shape priors for visual vehicle pose and shape estimation
Rui Wang, Nan Yang, Joerg Stueckler, and Daniel Cremers · 2020
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Point cloud completion by skip-attention network with hierarchical folding
Xin Wen, Tianyang Li, Zhizhong Han, and Yu-Shen Liu · 2020
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Grnet: Gridding residual network for dense point cloud completion
Haozhe Xie, Hongxun Yao, Shangchen Zhou, Jiageng Mao, Shengping Zhang, and Wenxiu Sun · 2020
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Autolabeling 3d objects with differentiable rendering of sdf shape priors
Sergey Zakharov, Wadim Kehl, Arjun Bhargava, and Adrien Gaidon · 2020
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In-domain gan inversion for real image editing
Jiapeng Zhu, Yujun Shen, Deli Zhao, and Bolei Zhou · 2020
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Fostering generalization in single-view 3d reconstruction by learning a hierarchy of local and global shape priors
Jan Bechtold, Tatarchenko Maxim, Fischer Volker, and Brox Thomas · 2021
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On the effectiveness of weight-encoded neural implicit 3d shapes, 2021
Thomas Davies, Derek Nowrouzezahrai, and Alec Jacobson · 2021
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Deep optimized priors for 3d shape modeling and reconstruction
Mingyue Yang, Yuxin Wen, Weikai Chen, Yongwei Chen, and Kui Jia · 2021
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