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With the recent advances in hardware and rendering techniques, 3D models have emerged everywhere in our life.
Surfaces for computer-aided design of space forms
Steven A Coons · 1967
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Superquadrics and angle-preserving transformations
Alan H Barr · 1981
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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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Sculpting: An interactive volumetric modeling technique
Tinsley A Galyean and John F Hughes · 1991
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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The ball-pivoting algorithm for surface reconstruction
Fausto Bernardini, Joshua Mittleman, Holly Rushmeier, Cláudio Silva, and Gabriel Taubin · 1999
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Unstructured lumigraph rendering
Chris Buehler, Michael Bosse, Leonard McMillan, Steven Gortler, and Michael Cohen · 2001
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Geometry images
Xianfeng Gu, Steven J Gortler, and Hugues Hoppe · 2002
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Dual contouring of Hermite data
Tao Ju, Frank Losasso, Scott Schaefer, and Joe Warren · 2002
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Delaunay triangulation based surface reconstruction
Frédéric Cazals and Joachim Giesen · 2006
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Poisson surface reconstruction
Michael Kazhdan, Matthew Bolitho, and Hugues Hoppe · 2006
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Provably good moving least squares
Ravikrishna Kolluri · 2008
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Screened poisson surface reconstruction
Michael Kazhdan and Hugues Hoppe · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
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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, Jianxiong Xiao, Li Yi, and Fisher Yu · 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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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 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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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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Structure-from-motion revisited
Johannes Lutz Schönberger and Jan-Michael Frahm · 2016
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Pixelwise view selection for unstructured multi-view stereo
Johannes Lutz Schönberger, Enliang Zheng, Marc Pollefeys, and Jan-Michael Frahm · 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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Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 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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Octnetfusion: Learning depth fusion from data
Gernot Riegler, Ali Osman Ulusoy, Horst Bischof, and Andreas Geiger · 2017
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Surfnet: Generating 3d shape surfaces using deep residual networks
Ayan Sinha, Asim Unmesh, Qixing Huang, and Karthik Ramani · 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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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 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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Learning to reconstruct high-quality 3d shapes with cascaded fully convolutional networks
Yan-Pei Cao, Zheng-Ning Liu, Zheng-Fei Kuang, Leif Kobbelt, and Shi-Min Hu · 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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Deep marching cubes: Learning explicit surface representations
Yiyi Liao, Simon Donne, and Andreas Geiger · 2018
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Im2struct: Recovering 3d shape structure from a single rgb image
Chengjie Niu, Jun Li, and Kai Xu · 2018
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Learning to estimate 3d human pose and shape from a single color image
Georgios Pavlakos, Luyang Zhu, Xiaowei Zhou, and Kostas Daniilidis · 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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Csgnet: Neural shape parser for constructive solid geometry
Gopal Sharma, Rishabh Goyal, Difan Liu, Evangelos Kalogerakis, and Subhransu Maji · 2018
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Deep image prior
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 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 shape priors for single-view 3d completion and reconstruction
Jiajun Wu, Chengkai Zhang, Xiuming Zhang, Zhoutong Zhang, William T Freeman, and Joshua B Tenenbaum · 2018
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P2p-net: Bidirectional point displacement net for shape transform
Kangxue Yin, Hui Huang, Daniel Cohen-Or, and Hao Zhang · 2018
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Learning to reconstruct shapes from unseen classes
Xiuming Zhang, Zhoutong Zhang, Chengkai Zhang, Josh Tenenbaum, Bill Freeman, and Jiajun Wu · 2018
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Multi-garment net: Learning to dress 3d people from images
Bharat Lal Bhatnagar, Garvita Tiwari, Christian Theobalt, and Gerard Pons-Moll · 2019
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Learning to predict 3d objects with an interpolation-based differentiable renderer
Wenzheng Chen, Huan Ling, Jun Gao, 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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Scan2mesh: From unstructured range scans to 3d meshes
Angela Dai and Matthias Nießner · 2019
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Accurate 3d face reconstruction with weakly-supervised learning: From single image to image set
Yu Deng, Jiaolong Yang, Sicheng Xu, Dong Chen, Yunde Jia, and Xin Tong · 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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Learning shape templates with structured implicit functions
Kyle Genova, Forrester Cole, Daniel Vlasic, Aaron Sarna, William T Freeman, and Thomas Funkhouser · 2019
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Mesh r-cnn
Georgia Gkioxari, Jitendra Malik, and Justin Johnson · 2019
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Convolutional mesh regression for single-image human shape reconstruction
Nikos Kolotouros, Georgios Pavlakos, and Kostas Daniilidis · 2019
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Supervised fitting of geometric primitives to 3d point clouds
Lingxiao Li, Minhyuk Sung, Anastasia Dubrovina, Li Yi, and Leonidas J Guibas · 2019
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Photometric mesh optimization for video-aligned 3d object reconstruction
Chen-Hsuan Lin, Oliver Wang, Bryan C Russell, Eli Shechtman, Vladimir G Kim, Matthew Fisher, and Simon Lucey · 2019
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Deep meta functionals for shape representation
Gidi Littwin and Lior Wolf · 2019
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Soft rasterizer: A differentiable renderer for image-based 3d reasoning
Shichen Liu, Tianye Li, Weikai Chen, and Hao Li · 2019
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Learning to infer implicit surfaces without 3d supervision
Shichen Liu, Shunsuke Saito, Weikai Chen, and Hao Li · 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
Cited alongside, same era.
Implicit surface representations as layers in neural networks
Mateusz Michalkiewicz, Jhony K Pontes, Dominic Jack, Mahsa Baktashmotlagh, and Anders Eriksson · 2019
Cited alongside, same era.
Texture fields: Learning texture representations in function space
Michael Oechsle, Lars Mescheder, Michael Niemeyer, Thilo Strauss, and Andreas Geiger · 2019
Cited alongside, same era.
Deep mesh reconstruction from single rgb images via topology modification networks
Junyi Pan, Xiaoguang Han, Weikai Chen, Jiapeng Tang, and Kui Jia · 2019
Cited alongside, same era.
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.
Needrop: Self-supervised shape representation from sparse point clouds using needle dropping
Alexandre Boulch, Pierre-Alain Langlois, Gilles Puy, and Renaud Marlet · 2021
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Dib-r++: Learning to predict lighting and material with a hybrid differentiable renderer
Wenzheng Chen, Joey Litalien, Jun Gao, Zian Wang, Clement Fuji Tsang, Sameh Khamis, Or Litany, and Sanja Fidler · 2021
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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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Neural marching cubes
Zhiqin Chen and Hao Zhang · 2021
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Neural lumigraph rendering
Petr Kellnhofer, Lars C Jebe, Andrew Jones, Ryan Spicer, Kari Pulli, and Gordon Wetzstein · 2021
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Cpfn: Cascaded primitive fitting networks for high-resolution point clouds
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Despoina Paschalidou, Ali Osman Ulusoy, and Andreas Geiger · 2019
Cited alongside, same era.
Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization
Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Angjoo Kanazawa, and Hao Li · 2019
Cited alongside, same era.
A skeleton-bridged deep learning approach for generating meshes of complex topologies from single rgb images
Jiapeng Tang, Xiaoguang Han, Junyi Pan, Kui Jia, and Xin Tong · 2019
Cited alongside, same era.
What do single-view 3d reconstruction networks learn?
Maxim Tatarchenko, Stephan R Richter, René Ranftl, Zhuwen Li, Vladlen Koltun, and Thomas Brox · 2019
Cited alongside, same era.
3dn: 3d deformation network
Weiyue Wang, Duygu Ceylan, Radomir Mech, and Ulrich Neumann · 2019
Cited alongside, same era.
Pixel2mesh++: Multi-view 3d mesh generation via deformation
Chao Wen, Yinda Zhang, Zhuwen Li, and Yanwei Fu · 2019
Cited alongside, same era.
Deep geometric prior for surface reconstruction
Francis Williams, Teseo Schneider, Claudio Silva, Denis Zorin, Joan Bruna, and Daniele Panozzo · 2019
Cited alongside, same era.
Eric-Tuan Lê, Minhyuk Sung, Duygu Ceylan, Radomir Mech, Tamy Boubekeur, and Niloy J Mitra · 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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Deep implicit moving least-squares functions for 3d reconstruction
Shi-Lin Liu, Hao-Xiang Guo, Hao Pan, Peng-Shuai Wang, Xin Tong, and Yang Liu · 2021
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Deepdt: Learning geometry from delaunay triangulation for surface reconstruction
Yiming Luo, Zhenxing Mi, and Wenbing Tao · 2021
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Nerf in the wild: Neural radiance fields for unconstrained photo collections
Ricardo Martin-Brualla, Noha Radwan, Mehdi SM Sajjadi, Jonathan T Barron, Alexey Dosovitskiy, and Daniel Duckworth · 2021
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Unisurf: Unifying neural implicit surfaces and radiance fields for multi-view reconstruction
Michael Oechsle, Songyou Peng, and Andreas Geiger · 2021
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Shape as points: A differentiable poisson solver
Songyou Peng, Chiyu Jiang, Yiyi Liao, Michael Niemeyer, Marc Pollefeys, and Andreas Geiger · 2021
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Learning delaunay surface elements for mesh reconstruction
Marie-Julie Rakotosaona, Paul Guerrero, Noam Aigerman, Niloy J Mitra, and Maks Ovsjanikov · 2021
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Csg-stump: A learning friendly csg-like representation for interpretable shape parsing
Daxuan Ren, Jianmin Zheng, Jianfei Cai, Jiatong Li, Haiyong Jiang, Zhongang Cai, Junzhe Zhang, Liang Pan, Mingyuan Zhang, Haiyu Zhao, et al · 2021
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Deep marching tetrahedra: a hybrid representation for high-resolution 3d shape synthesis
Tianchang Shen, Jun Gao, Kangxue Yin, Ming-Yu Liu, and Sanja Fidler · 2021
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Geometric granularity aware pixel-to-mesh
Yue Shi, Bingbing Ni, Jinxian Liu, Dingyi Rong, Ye Qian, and Wenjun Zhang · 2021
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Retrievalfuse: Neural 3d scene reconstruction with a database
Yawar Siddiqui, Justus Thies, Fangchang Ma, Qi Shan, Matthias Nießner, and Angela Dai · 2021
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Growing 3d artefacts and functional machines with neural cellular automata
Shyam Sudhakaran, Djordje Grbic, Siyan Li, Adam Katona, Elias Najarro, Claire Glanois, and Sebastian Risi · 2021
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Octfield: Hierarchical implicit functions for 3d modeling
Jia-Heng Tang, Weikai Chen, Bo Wang, Songrun Liu, Bo Yang, Lin Gao, et al · 2021
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Sa-convonet: Sign-agnostic optimization of convolutional occupancy networks
Jiapeng Tang, Jiabao Lei, Dan Xu, Feiying Ma, Kui Jia, and Lei Zhang · 2021
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Adaptive surface reconstruction with multiscale convolutional kernels
Benjamin Ummenhofer and Vladlen Koltun · 2021
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Multi-view 3d reconstruction with transformers
Dan Wang, Xinrui Cui, Xun Chen, Zhengxia Zou, Tianyang Shi, Septimiu Salcudean, Z Jane Wang, and Rabab Ward · 2021
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Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction
Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, and Wenping Wang · 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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Deepcad: A deep generative network for computer-aided design models
Rundi Wu, Chang Xiao, and Changxi Zheng · 2021
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Hpnet: Deep primitive segmentation using hybrid representations
Siming Yan, Zhenpei Yang, Chongyang Ma, Haibin Huang, Etienne Vouga, and Qixing Huang · 2021
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Volume rendering of neural implicit surfaces
Lior Yariv, Jiatao Gu, Yoni Kasten, and Yaron Lipman · 2021
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Ners: Neural reflectance surfaces for sparse-view 3d reconstruction in the wild
Jason Zhang, Gengshan Yang, Shubham Tulsiani, and Deva Ramanan · 2021
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Learning signed distance field for multi-view surface reconstruction
Jingyang Zhang, Yao Yao, and Long Quan · 2021
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Sign-agnostic implicit learning of surface self-similarities for shape modeling and reconstruction from raw point clouds
Wenbin Zhao, Jiabao Lei, Yuxin Wen, Jianguo Zhang, and Kui Jia · 2021
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Neural rgb-d surface reconstruction
Dejan Azinović, Ricardo Martin-Brualla, Dan B Goldman, Matthias Nießner, and Justus Thies · 2022
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Differentiable rendering of neural sdfs through reparameterization
Sai Praveen Bangaru, Michael Gharbi, Fujun Luan, Tzu-Mao Li, Kalyan Sunkavalli, Milos Hasan, Sai Bi, Zexiang Xu, Gilbert Bernstein, and Fredo Durand · 2022
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Poco: Point convolution for surface reconstruction
Alexandre Boulch and Renaud Marlet · 2022
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Zhiqin Chen, Thomas Funkhouser, Peter Hedman, and Andrea Tagliasacchi · 2022
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Neural dual contouring
Zhiqin Chen, Andrea Tagliasacchi, Thomas Funkhouser, and Hao Zhang · 2022
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Improving neural implicit surfaces geometry with patch warping
François Darmon, Bénédicte Bascle, Jean-Clément Devaux, Pascal Monasse, and Mathieu Aubry · 2022
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Geo-neus: Geometry-consistent neural implicit surfaces learning for multi-view reconstruction
Qiancheng Fu, Qingshan Xu, Yew-Soon Ong, and Wenbing Tao · 2022
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Differentiable stereopsis: Meshes from multiple views using differentiable rendering
Shubham Goel, Georgia Gkioxari, and Jitendra Malik · 2022
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Meshudf: Fast and differentiable meshing of unsigned distance field networks
Benoit Guillard, Federico Stella, and Pascal Fua · 2022
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Complexgen: Cad reconstruction by b-rep chain complex generation
Haoxiang Guo, Shilin Liu, Hao Pan, Yang Liu, Xin Tong, and Baining Guo · 2022
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Neural 3d scene reconstruction with the manhattan-world assumption
Haoyu Guo, Sida Peng, Haotong Lin, Qianqian Wang, Guofeng Zhang, Hujun Bao, and Xiaowei Zhou · 2022
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Neural template: Topology-aware reconstruction and disentangled generation of 3d meshes
Ka-Hei Hui, Ruihui Li, Jingyu Hu, and Chi-Wing Fu · 2022
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Snes: Learning probably symmetric neural surfaces from incomplete data
Eldar Insafutdinov, Dylan Campbell, João F Henriques, and Andrea Vedaldi · 2022
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Reconstructing editable prismatic cad from rounded voxel models
Joseph George Lambourne, Karl Willis, Pradeep Kumar Jayaraman, Longfei Zhang, Aditya Sanghi, and Kamal Rahimi Malekshan · 2022
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2d gans meet unsupervised single-view 3d reconstruction
Feng Liu and Xiaoming Liu · 2022
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Sparseneus: Fast generalizable neural surface reconstruction from sparse views
Xiaoxiao Long, Cheng Lin, Peng Wang, Taku Komura, and Wenping Wang · 2022
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Autosdf: Shape priors for 3d completion, reconstruction and generation
Paritosh Mittal, Yen-Chi Cheng, Maneesh Singh, and Shubham Tulsiani · 2022
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Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency
Tom Monnier, Matthew Fisher, Alexei A. Efros, and Mathieu Aubry · 2022
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Instant neural graphics primitives with a multiresolution hash encoding
Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller · 2022
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Extracting triangular 3d models, materials, and lighting from images
Jacob Munkberg, Jon Hasselgren, Tianchang Shen, Jun Gao, Wenzheng Chen, Alex Evans, Thomas Müller, and Sanja Fidler · 2022
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Extrudenet: Unsupervised inverse sketch-and-extrude for shape parsing
Daxuan Ren, Jianmin Zheng, Jianfei Cai, Jiatong Li, and Junzhe Zhang · 2022
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Neural 3d reconstruction in the wild
Jiaming Sun, Xi Chen, Qianqian Wang, Zhengqi Li, Hadar Averbuch-Elor, Xiaowei Zhou, and Noah Snavely · 2022
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Point2cyl: Reverse engineering 3d objects from point clouds to extrusion cylinders
Mikaela Angelina Uy, Yen-Yu Chang, Minhyuk Sung, Purvi Goel, Joseph G Lambourne, Tolga Birdal, and Leonidas J Guibas · 2022
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Dual octree graph networks for learning adaptive volumetric shape representations
Peng-Shuai Wang, Yang Liu, and Xin Tong · 2022
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Hf-neus: Improved surface reconstruction using high-frequency details
Yiqun Wang, Ivan Skorokhodov, and Peter Wonka · 2022
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Neural fields as learnable kernels for 3d reconstruction
Francis Williams, Zan Gojcic, Sameh Khamis, Denis Zorin, Joan Bruna, Sanja Fidler, and Or Litany · 2022
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Multi-view mesh reconstruction with neural deferred shading
Markus Worchel, Rodrigo Diaz, Weiwen Hu, Oliver Schreer, Ingo Feldmann, and Peter Eisert · 2022
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Neural fields in visual computing and beyond
Yiheng Xie, Towaki Takikawa, Shunsuke Saito, Or Litany, Shiqin Yan, Numair Khan, Federico Tombari, James Tompkin, Vincent Sitzmann, and Srinath Sridhar · 2022
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Gifs: Neural implicit function for general shape representation
Jianglong Ye, Yuntao Chen, Naiyan Wang, and Xiaolong Wang · 2022
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Capri-net: Learning compact cad shapes with adaptive primitive assembly
Fenggen Yu, Zhiqin Chen, Manyi Li, Aditya Sanghi, Hooman Shayani, Ali Mahdavi-Amiri, and Hao Zhang · 2022
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Monosdf: Exploring monocular geometric cues for neural implicit surface reconstruction
Zehao Yu, Songyou Peng, Michael Niemeyer, Torsten Sattler, and Andreas Geiger · 2022
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Critical regularizations for neural surface reconstruction in the wild
Jingyang Zhang, Yao Yao, Shiwei Li, Tian Fang, David McKinnon, Yanghai Tsin, and Long Quan · 2022
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