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We introduce DMTet, a deep 3D conditional generative model that can synthesize high-resolution 3D shapes using simple user guides such as coarse voxels.
Smooth subdivision surfaces based on triangles
Charles Loop · 1987
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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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An efficient method of triangulating equi-valued surfaces by using tetrahedral cells
Akio Doi and Akio Koide · 1991
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On visual similarity based 3d model retrieval
Ding-Yun Chen, Xiao-Pei Tian, Yu-Te Shen, and Ming Ouhyoung · 2003
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Isosurface stuffing improved: acute lattices and feature matching
Crawford Doran, Athena Chang, and Robert Bridson · 2013
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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
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Voxnet: A 3d convolutional neural network for real-time object recognition
Daniel Maturana and Sebastian Scherer · 2015
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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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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
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Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling
Jiajun Wu, Chengkai Zhang, Tianfan Xue, William T Freeman, and Joshua B Tenenbaum · 2016
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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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Hierarchical surface prediction for 3d object reconstruction
Christian Häne, Shubham Tulsiani, and Jitendra Malik · 2017
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Least squares generative adversarial networks
Xudong Mao, Qing Li, Haoran Xie, Raymond YK Lau, Zhen Wang, and Stephen Paul Smolley · 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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Complementme: Weakly-supervised component suggestions for 3d modeling
Minhyuk Sung, Hao Su, Vladimir G Kim, Siddhartha Chaudhuri, and Leonidas Guibas · 2017
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Octree generating networks: Efficient convolutional architectures for high-resolution 3d outputs
M. Tatarchenko, A. Dosovitskiy, and T. 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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O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis
Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun, and Xin Tong · 2017
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Scancomplete: Large-scale scene completion and semantic segmentation for 3d scans
Angela Dai, Daniel Ritchie, Martin Bokeloh, Scott Reed, Jürgen Sturm, and Matthias Nießner · 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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Deep marching cubes: Learning explicit surface representations
Yiyi Liao, Simon Donné, and Andreas Geiger · 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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SCORES: Shape composition with recursive substructure priors
Chenyang Zhu, Kai Xu, Siddhartha Chaudhuri, Renjiao Yi, and Hao Zhang · 2018
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Learning to predict 3d objects with an interpolation-based differentiable renderer
Wenzheng Chen, Jun Gao, Huan Ling, Edward Smith, Jaakko Lehtinen, Alec Jacobson, and Sanja Fidler · 2019
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Learning implicit fields for generative shape modeling
Zhiqin Chen and Hao Zhang · 2019
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Beyond fixed grid: Learning geometric image representation with a deformable grid
Jun Gao, Zian Wang, Jinchen Xuan, and Sanja Fidler · 2020
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Point2mesh: A self-prior for deformable meshes
Rana Hanocka, Gal Metzer, Raja Giryes, and Daniel Cohen-Or · 2020
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Dualsdf: Semantic shape manipulation using a two-level representation
Zekun Hao, Hadar Averbuch-Elor, Noah Snavely, and Serge Belongie · 2020
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Adversarial generation of continuous implicit shape representations
Marian Kleineberg, Matthias Fey, and Frank Weichert · 2020
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Analytic marching: An analytic meshing solution from deep implicit surface networks
Jiabao Lei and Kui Jia · 2020
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Neural subdivision
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Jun Gao, Chengcheng Tang, Vignesh Ganapathi-Subramanian, Jiahui Huang, Hao Su, and Leonidas J Guibas · 2019
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Sdm-net: Deep generative network for structured deformable mesh
Lin Gao, Jie Yang, Tong Wu, Yu-Jie Yuan, Hongbo Fu, Yu-Kun Lai, and Hao Zhang · 2019
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Mesh r-cnn
Georgia Gkioxari, Jitendra Malik, and Justin Johnson · 2019
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Kaolin: A pytorch library for accelerating 3d deep learning research
Krishna Murthy J., Edward Smith, Jean-Francois Lafleche, Clement Fuji Tsang, Artem Rozantsev, Wenzheng Chen, Tommy Xiang, Rev Lebaredian, and Sanja Fidler · 2019
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Fast interactive object annotation with curve-gcn
Huan Ling, Jun Gao, Amlan Kar, Wenzheng Chen, and Sanja Fidler · 2019
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Point-voxel cnn for efficient 3d deep learning
Zhijian Liu, Haotian Tang, Yujun Lin, and Song Han · 2019
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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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Hsueh-Ti Derek Liu, Vladimir G. Kim, Siddhartha Chaudhuri, Noam Aigerman, and Alec Jacobson · 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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Polygen: An autoregressive generative model of 3d meshes
Charlie Nash, Yaroslav Ganin, S. M. Ali Eslami, and Peter W. Battaglia · 2020
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Convolutional occupancy networks
Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger · 2020
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Meshsdf: Differentiable iso-surface extraction
Edoardo Remelli, Artem Lukoianov, Stephan Richter, Benoit Guillard, Timur Bagautdinov, Pierre Baque, and Pascal Fua · 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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Implicit neural representations with periodic activation functions
Vincent Sitzmann, Julien N.P. Martel, Alexander W. Bergman, David B. Lindell, and Gordon Wetzstein · 2020
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Deep octree-based cnns with output-guided skip connections for 3d shape and scene completion
Peng-Shuai Wang, Yang Liu, and Xin Tong · 2020
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Coalesce: Component assembly by learning to synthesize connections
Kangxue Yin, Zhiqin Chen, Siddhartha Chaudhuri, Matthew Fisher, Vladimir Kim, and Hao Zhang · 2020
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Decor-gan: 3d shape detailization by conditional refinement
Zhiqin Chen, Vladimir G. Kim, Matthew Fisher, Noam Aigerman, Hao Zhang, and Siddhartha Chaudhuri · 2021
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Deformed implicit field: Modeling 3d shapes with learned dense correspondence
Yu Deng, Jiaolong Yang, and Xin Tong · 2021
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D2im-net: Learning detail disentangled implicit fields from single images
Manyi Li and Hao Zhang · 2021
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Neural parts: Learning expressive 3d shape abstractions with invertible neural networks
Despoina Paschalidou, Angelos Katharopoulos, Andreas Geiger, and Sanja Fidler · 2021
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Neural geometric level of detail: Real-time rendering with implicit 3D shapes
Towaki Takikawa, Joey Litalien, Kangxue Yin, Karsten Kreis, Charles Loop, Derek Nowrouzezahrai, Alec Jacobson, Morgan McGuire, and Sanja Fidler · 2021
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Neural splines: Fitting 3d surfaces with infinitely-wide neural networks
Francis Williams, Matthew Trager, Joan Bruna, and Denis Zorin · 2021
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