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Any solid object can be decomposed into a collection of convex polytopes (in short, convexes).
Machine perception of three-dimensional solids
Lawrence G Roberts · 1963
Earlier work this paper cites.
Visual perception by computer
Thomas Binford · 1971
Earlier work this paper cites.
An efficient algorithm for determining the convex hull of a finite planar set
Ronald L. Graham · 1972
Earlier work this paper cites.
Convex hulls of finite sets of points in two and three dimensions
Franco P Preparata and Se June Hong · 1977
Earlier work this paper cites.
Convex decompositions of polyhedra
Bernard M Chazelle · 1981
Earlier work this paper cites.
A generalization of algebraic surface drawing
James F Blinn · 1982
Earlier work this paper cites.
Constructive solid geometry for polyhedral objects
David H Laidlaw, W Benjamin Trumbore, and John F Hughes · 1986
Earlier work this paper cites.
Recognition-by-components: a theory of human image understanding
Irving Biederman · 1987
Earlier work this paper cites.
Marching cubes: A high resolution 3d surface construction algorithm
William E Lorensen and Harvey E Cline · 1987
Earlier work this paper cites.
A fast procedure for computing the distance between complex objects in three-dimensional space
Elmer G Gilbert, Daniel W Johnson, and S Sathiya Keerthi · 1988
Earlier work this paper cites.
Surface reconstruction from unorganized points
Hugues Hoppe, Tony DeRose, Tom Duchamp, John McDonald, and Werner Stuetzle · 1992
Earlier work this paper cites.
Neural networks for convex hull computation
Yee Leung, Jiang-She Zhang, and Zong-Ben Xu · 1997
Earlier work this paper cites.
A morphable model for the synthesis of 3D faces
Volker Blanz and Thomas Vetter · 1999
Earlier work this paper cites.
Approximate convex decomposition of polyhedra
Jyh-Ming Lien and Nancy M Amato · 2007
Earlier work this paper cites.
A simple and efficient approach for 3d mesh approximate convex decomposition
Khaled Mamou and Faouzi Ghorbel · 2009
Earlier work this paper cites.
Polygon mesh processing
Mario Botsch, Leif Kobbelt, Mark Pauly, Pierre Alliez, and Bruno Lévy · 2010
Earlier work this paper cites.
Kinectfusion: Real-time dense surface mapping and tracking
Richard A Newcombe, Shahram Izadi, Otmar Hilliges, David Molyneaux, David Kim, Andrew J Davison, Pushmeet Kohi, Jamie Shotton, Steve Hodges, and Andrew Fitzgibbon · 2011
Earlier work this paper cites.
Character Modeling with Maya and ZBrush: Professional polygonal modeling techniques
Jason Patnode · 2012
Earlier work this paper cites.
Fast approximate convex decomposition using relative concavity
Mukulika Ghosh, Nancy M Amato, Yanyan Lu, and Jyh-Ming Lien · 2013
Earlier work this paper cites.
Shapenet: An information-rich 3d model repository
Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al · 2015
Earlier work this paper cites.
3d mesh labeling via deep convolutional neural networks
Kan Guo, Dongqing Zou, and Xiaowu Chen · 2015
Earlier work this paper cites.
Geodesic convolutional neural networks on riemannian manifolds
Jonathan Masci, Davide Boscaini, Michael Bronstein, and Pierre Vandergheynst · 2015
Earlier work this paper cites.
Voxnet: A 3d convolutional neural network for real-time object recognition
Daniel Maturana and Sebastian Scherer · 2015
Earlier work this paper cites.
Dynamicfusion: Reconstruction and tracking of non-rigid scenes in real-time
Richard A Newcombe, Dieter Fox, and Steven M Seitz · 2015
Earlier work this paper cites.
Towards probabilistic volumetric reconstruction using ray potentials
Ali Osman Ulusoy, Andreas Geiger, and Michael J Black · 2015
Earlier work this paper cites.
Part-based modelling of compound scenes from images
Anton van den Hengel, Chris Russell, Anthony Dick, John Bastian, Daniel Pooley, Lachlan Fleming, and Lourdes Agapito · 2015
Earlier work this paper cites.
3d shapenets: A deep representation for volumetric shapes
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao · 2015
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Keep it smpl: Automatic estimation of 3d human pose and shape from a single image
Federica Bogo, Angjoo Kanazawa, Christoph Lassner, Peter Gehler, Javier Romero, and Michael J Black · 2016
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Generative and discriminative voxel modeling with convolutional neural networks
Andrew Brock, Theodore Lim, James M Ritchie, and Nick Weston · 2016
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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
Cited alongside, same era.
Learning a predictable and generative vector representation for objects
Rohit Girdhar, David F Fouhey, Mikel Rodriguez, and Abhinav Gupta · 2016
Cited alongside, same era.
Learning representations and generative models for 3d point clouds
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas · 2018
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A papier-mache 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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Deep marching cubes: Learning explicit surface representations
Yiyi Liao, Simon Donne, and Andreas Geiger · 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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Occupancy networks: Learning 3d reconstruction in function space
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Nearly convex segmentation of polyhedra through convex ridge separation
Guilin Liu, Zhonghua Xi, and Jyh-Ming Lien · 2016
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Volumetric hierarchical approximate convex decomposition
Khaled Mamou, E Lengyel, and Ed AK Peters · 2016
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Volumetric and multi-view cnns for object classification on 3d data
Charles R Qi, Hao Su, Matthias Nießner, Angela Dai, Mengyuan Yan, and Leonidas J Guibas · 2016
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Unsupervised learning of 3d structure from images
Danilo Jimenez Rezende, SM Ali Eslami, Shakir Mohamed, Peter Battaglia, Max Jaderberg, and Nicolas Heess · 2016
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Deep sliding shapes for amodal 3d object detection in rgb-d images
Shuran Song and Jianxiong Xiao · 2016
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Sphere-meshes for real-time hand modeling and tracking
Anastasia Tkach, Mark Pauly, and Andrea Tagliasacchi · 2016
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Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling
Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, and Josh Tenenbaum · 2016
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Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger · 2018
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Generating 3d faces using convolutional mesh autoencoders
Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, and Michael J Black · 2018
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Csgnet: Neural shape parser for constructive solid geometry
Gopal Sharma, Rishabh Goyal, Difan Liu, Evangelos Kalogerakis, and Subhransu Maji · 2018
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Learning 3d shape completion from laser scan data with weak supervision
David Stutz and Andreas Geiger · 2018
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Approximate convex decomposition and transfer for animated meshes
Daniel Thul, Sohyeon Jeong, Marc Pollefeys, et al · 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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Bae-net: Branched autoencoder for shape co-segmentation
Zhiqin Chen, Kangxue Yin, Matthew Fisher, Siddhartha Chaudhuri, and Hao Zhang · 2019
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Learning implicit fields for generative shape modeling
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PyBullet, a python module for physics simulation for games, robotics and machine learning
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Multi-robot collaborative dense scene reconstruction
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Learning shape templates with structured implicit functions
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Interactive differentiable simulation
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What do single-view 3d reconstruction networks learn?
Maxim Tatarchenko, Stephan R Richter, René Ranftl, Zhuwen Li, Vladlen Koltun, and Thomas Brox · 2019
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