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Recent advances in localized implicit functions have enabled neural implicit representation to be scalable to large scenes.
Parametric correspondence and chamfer matching: Two new techniques for image matching
Harry G Barrow, Jay M Tenenbaum, Robert C Bolles, and Helen C Wolf · 1977
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The earth mover’s distance as a metric for image retrieval
Yossi Rubner, Carlo Tomasi, and Leonidas J Guibas · 2000
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Vdb: High-resolution sparse volumes with dynamic topology
Ken Museth · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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ShapeNet: An information-rich 3D model repository
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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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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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Perspective transformer nets: Learning single-view 3D object reconstruction without 3D supervision
Xinchen Yan, Jimei Yang, Ersin Yumer, Yijie Guo, and Honglak Lee · 2016
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A point set generation network for 3D object reconstruction from a single image
Haoqiang Fan, Hao Su, and Leonidas J Guibas · 2017
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Hierarchical surface prediction for 3d object reconstruction
Christian Häne, Shubham Tulsiani, and Jitendra Malik · 2017
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GRASS: Generative recursive autoencoders for shape structures
Jun Li, Kai Xu, Siddhartha Chaudhuri, Ersin Yumer, Hao Zhang, and Leonidas Guibas · 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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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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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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MarrNet: 3D shape reconstruction via 2.5D sketches
Jiajun Wu, Yifan Wang, Tianfan Xue, Xingyuan Sun, Bill Freeman, and Josh Tenenbaum · 2017
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Learning representations and generative models for 3D point clouds
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas · 2018
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Deep learning advances on different 3D data representations: A survey
Eman Ahmed, Alexandre Saint, Abd El Rahman Shabayek, Kseniya Cherenkova, Rig Das, Gleb Gusev, Djamila Aouada, and Björn Ottersten · 2018
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3D semantic segmentation with submanifold sparse convolutional networks
Benjamin Graham, Martin Engelcke, and Laurens van der Maaten · 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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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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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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Deep local shapes: Learning local sdf priors for detailed 3d reconstruction
Rohan Chabra, Jan E Lenssen, Eddy Ilg, Tanner Schmidt, Julian Straub, Steven Lovegrove, and Richard Newcombe · 2020
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Bsp-net: Generating compact meshes via binary space partitioning
Zhiqin Chen, Andrea Tagliasacchi, and Hao Zhang · 2020
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Implicit functions in feature space for 3d shape reconstruction and completion
Julian Chibane, Thiemo Alldieck, and Gerard Pons-Moll · 2020
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Implicit functions in feature space for 3d shape reconstruction and completion
Julian Chibane, Thiemo Alldieck, and Gerard Pons-Moll · 2020
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Curriculum DeepSDF, 2020
Yueqi Duan, Haidong Zhu, He Wang, Li Yi, Ram Nevatia, and Leonidas J. Guibas · 2020
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Local deep implicit functions for 3d shape
Kyle Genova, Forrester Cole, Avneesh Sud, Aaron Sarna, and Thomas Funkhouser · 2020
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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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FoldingNet: Point cloud auto-encoder via deep grid deformation
Yaoqing Yang, Chen Feng, Yiru Shen, and Dong Tian · 2018
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Learning implicit fields for generative shape modeling
Zhiqin Chen and Hao Zhang · 2019
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4D spatio-temporal convnets: Minkowski convolutional neural networks
Christopher Choy, JunYoung Gwak, and Silvio Savarese · 2019
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Scan2mesh: From unstructured range scans to 3D meshes
Angela Dai and Matthias Nießner · 2019
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Learning elementary structures for 3D shape generation and matching
Theo Deprelle, Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, and Mathieu Aubry · 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(Richard) Zhang · 2019
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Manifoldplus: A robust and scalable watertight manifold surface generation method for triangle soups
Jingwei Huang, Yichao Zhou, and Leonidas Guibas · 2020
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Local implicit grid representations for 3d scenes
Chiyu Jiang, Avneesh Sud, Ameesh Makadia, Jingwei Huang, Matthias Nießner, and Thomas Funkhouser · 2020
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3d reconstruction using deep learning: a survey
Yiwei Jin, Diqiong Jiang, and Ming Cai · 2020
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Neural sparse voxel fields
Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt · 2020
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Structedit: Learning structural shape variations
Kaichun Mo, Paul Guerrero, Li Yi, Hao Su, Peter Wonka, Niloy J Mitra, and Leonidas J Guibas · 2020
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Pt2pc: Learning to generate 3d point cloud shapes from part tree conditions
Kaichun Mo, He Wang, Xinchen Yan, and Leonidas Guibas · 2020
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Polygen: An autoregressive generative model of 3d meshes
Charlie Nash, Yaroslav Ganin, SM Ali Eslami, and Peter 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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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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A survey on deep geometry learning: From a representation perspective
Yun-Peng Xiao, Yu-Kun Lai, Fang-Lue Zhang, Chunpeng Li, and Lin Gao · 2020
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Dsm-net: Disentangled structured mesh net for controllable generation of fine geometry
Jie Yang, Kaichun Mo, Yu-Kun Lai, Leonidas J Guibas, and Lin Gao · 2020
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3d-front: 3d furnished rooms with layouts and semantics
Huan Fu, Bowen Cai, Lin Gao, Ling-Xiao Zhang, Jiaming Wang, Cao Li, Qixun Zeng, Chengyue Sun, Rongfei Jia, Binqiang Zhao, et al · 2021
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TM-NET: Deep generative networks for textured meshes
Lin Gao, Tong Wu, Yu-Jie Yuan, Ming-Xian Lin, Yu-Kun Lai, 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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Acorn: Adaptive coordinate networks for neural scene representation
Julien N. P. Martel, David B. Lindell, Connor Z. Lin, Eric R. Chan, Marco Monteiro, and Gordon Wetzstein · 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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