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We propose a differentiable sphere tracing algorithm to bridge the gap between inverse graphics methods and the recently proposed deep learning based implicit signed distance function.
Geometric modeling for computer vision
Bruce Guenther Baumgart · 1974
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Sphere tracing: A geometric method for the antialiased ray tracing of implicit surfaces
John C Hart · 1996
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Inverse global illumination: Recovering reflectance models of real scenes from photographs
Yizhou Yu, Paul Debevec, Jitendra Malik, and Tim Hawkins · 1999
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A survey of inverse rendering problems
Gustavo Patow and Xavier Pueyo · 2003
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A comparison and evaluation of multi-view stereo reconstruction algorithms
Steven M Seitz, Brian Curless, James Diebel, Daniel Scharstein, and Richard Szeliski · 2006
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Multiview photometric stereo
Carlos Hernandez, George Vogiatzis, and Roberto Cipolla · 2008
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Opendr: An approximate differentiable renderer
Matthew M Loper and Michael J Black · 2014
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A new variational framework for multiview surface reconstruction
Ben Semerjian · 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, et al · 2015
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Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
David Eigen and Rob Fergus · 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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A large dataset of object scans
Sungjoon Choi, Qian-Yi Zhou, Stephen Miller, and Vladlen Koltun · 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 predictable and generative vector representation for objects
Rohit Girdhar, David F Fouhey, Mikel Rodriguez, and Abhinav Gupta · 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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Polarimetric multi-view stereo
Zhaopeng Cui, Jinwei Gu, Boxin Shi, Ping Tan, and Jan Kautz · 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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Hierarchical surface prediction for 3d object reconstruction
Christian Häne, Shubham Tulsiani, and Jitendra Malik · 2017
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Scaling cnns for high resolution volumetric reconstruction from a single image
Adrian Johnston, Ravi Garg, Gustavo Carneiro, Ian Reid, and Anton van den Hengel · 2017
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Using locally corresponding cad models for dense 3d reconstructions from a single image
Chen Kong, Chen-Hsuan Lin, and Simon Lucey · 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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Octree generating networks: Efficient convolutional architectures for high-resolution 3d outputs
Maxim Tatarchenko, Alexey Dosovitskiy, and Thomas Brox · 2017
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3dmatch: Learning local geometric descriptors from rgb-d reconstructions
Dpsnet: end-to-end deep plane sweep stereo
Sunghoon Im, Hae-Gon Jeon, Stephen Lin, and In So Kweon · 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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Soft rasterizer: Differentiable rendering for unsupervised single-view mesh reconstruction
Shichen Liu, Weikai Chen, Tianye Li, 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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Neural volumes: Learning dynamic renderable volumes from images
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Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger · 2019
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Andy Zeng, Shuran Song, Matthias Nießner, Matthew Fisher, Jianxiong Xiao, and Thomas Funkhouser · 2017
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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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Deepmvs: Learning multi-view stereopsis
Po-Han Huang, Kevin Matzen, Johannes Kopf, Narendra Ahuja, and Jia-Bin Huang · 2018
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Unsupervised learning of shape and pose with differentiable point clouds
Eldar Insafutdinov and Alexey Dosovitskiy · 2018
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End-to-end recovery of human shape and pose
Angjoo Kanazawa, Michael J Black, David W Jacobs, and Jitendra Malik · 2018
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Neural 3d mesh renderer
Hiroharu Kato, Yoshitaka Ushiku, and Tatsuya Harada · 2018
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Differentiable monte carlo ray tracing through edge sampling
Tzu-Mao Li, Miika Aittala, Frédo Durand, and Jaakko Lehtinen · 2018
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Implicit surface representations as layers in neural networks
Mateusz Michalkiewicz, Jhony K Pontes, Dominic Jack, Mahsa Baktashmotlagh, and Anders Eriksson · 2019
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Occupancy flow: 4d reconstruction by learning particle dynamics
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Deepsdf: Learning continuous signed distance functions for shape representation
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Conditional single-view shape generation for multi-view stereo reconstruction
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Pixel2mesh++: Multi-view 3d mesh generation via deformation
Chao Wen, Yinda Zhang, Zhuwen Li, and Yanwei Fu · 2019
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Sdfdiff: Differentiable rendering of signed distance fields for 3d shape optimization
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