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We develop a generalized 3D shape generation prior model, tailored for multiple 3D tasks including unconditional shape generation, point cloud completion, and cross-modality shape generation, etc.
Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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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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Neural discrete representation learning
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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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Text2shape: Generating shapes from natural language by learning joint embeddings
Kevin Chen, Christopher B Choy, Manolis Savva, Angel X Chang, Thomas Funkhouser, and Silvio Savarese · 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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Priyanka Mandikal, KL Navaneet, Mayank Agarwal, and R Venkatesh Babu · 2018
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Pix3d: Dataset and methods for single-image 3d shape modeling
Xingyuan Sun, Jiajun Wu, Xiuming Zhang, Zhoutong Zhang, Chengkai Zhang, Tianfan Xue, Joshua B Tenenbaum, and William T Freeman · 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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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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Foldingnet: Point cloud auto-encoder via deep grid deformation
Yaoqing Yang, Chen Feng, Yiru Shen, and Dong Tian · 2018
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Pcn: Point completion network
Wentao Yuan, Tejas Khot, David Held, Christoph Mertz, and Martial Hebert · 2018
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Shapeglot: Learning language for shape differentiation
Panos Achlioptas, Judy Fan, Robert Hawkins, Noah Goodman, and Leonidas J Guibas · 2019
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Unpaired point cloud completion on real scans using adversarial training
Xuelin Chen, Baoquan Chen, and Niloy J Mitra · 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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3d point cloud generative adversarial network based on tree structured graph convolutions
Dong Wook Shu, Sung Woo Park, and Junseok Kwon · 2019
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Topnet: Structural point cloud decoder
Lyne P Tchapmi, Vineet Kosaraju, Hamid Rezatofighi, Ian Reid, and Silvio Savarese · 2019
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3dn: 3d deformation network
Weiyue Wang, Duygu Ceylan, Radomir Mech, and Ulrich Neumann · 2019
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Disn: Deep implicit surface network for high-quality single-view 3d reconstruction
Qiangeng Xu, Weiyue Wang, Duygu Ceylan, Radomir Mech, and Ulrich Neumann · 2019
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Pointflow: 3d point cloud generation with continuous normalizing flows
Guandao Yang, Xun Huang, Zekun Hao, Ming-Yu Liu, Serge Belongie, and Bharath Hariharan · 2019
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Learning gradient fields for shape generation
Ruojin Cai, Guandao Yang, Hadar Averbuch-Elor, Zekun Hao, Serge Belongie, Noah Snavely, and Bharath Hariharan · 2020
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Wavegrad: Estimating gradients for waveform generation
Nanxin Chen, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi, and William Chan · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Sdfdiff: Differentiable rendering of signed distance fields for 3d shape optimization
Yue Jiang, Dantong Ji, Zhizhong Han, and Matthias Zwicker · 2020
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Softflow: Probabilistic framework for normalizing flow on manifolds
Hyeongju Kim, Hyeonseung Lee, Woo Hyun Kang, Joun Yeop Lee, and Nam Soo Kim · 2020
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High-resolution image synthesis with latent diffusion models, 2021
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 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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Pointr: Diverse point cloud completion with geometry-aware transformers
Xumin Yu, Yongming Rao, Ziyi Wang, Zuyan Liu, Jiwen Lu, and Jie Zhou · 2021
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Unsupervised 3d shape completion through gan inversion
Junzhe Zhang, Xinyi Chen, Zhongang Cai, Liang Pan, Haiyu Zhao, Shuai Yi, Chai Kiat Yeo, Bo Dai, and Chen Change Loy · 2021
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3d shape generation and completion through point-voxel diffusion
Linqi Zhou, Yilun Du, and Jiajun Wu · 2021
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Roman Klokov, Edmond Boyer, and Jakob Verbeek · 2020
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Pointcleannet: Learning to denoise and remove outliers from dense point clouds
Marie-Julie Rakotosaona, Vittorio La Barbera, Paul Guerrero, Niloy J Mitra, and Maks Ovsjanikov · 2020
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Pq-net: A generative part seq2seq network for 3d shapes
Rundi Wu, Yixin Zhuang, Kai Xu, Hao Zhang, and Baoquan Chen · 2020
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Structured denoising diffusion models in discrete state-spaces
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg · 2021
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Crossvit: Cross-attention multi-scale vision transformer for image classification
Chun-Fu Richard Chen, Quanfu Fan, and Rameswar Panda · 2021
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Ilvr: Conditioning method for denoising diffusion probabilistic models
Jooyoung Choi, Sungwon Kim, Yonghyun Jeong, Youngjune Gwon, and Sungroh Yoon · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Oron Ashual, Shelly Sheynin, Adam Polyak, Uriel Singer, Oran Gafni, Eliya Nachmani, and Yaniv Taigman · 2022
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Shapecrafter: A recursive text-conditioned 3d shape generation model
Rao Fu, Xiao Zhan, Yiwen Chen, Daniel Ritchie, and Srinath Sridhar · 2022
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Vector quantized diffusion model for text-to-image synthesis
Shuyang Gu, Dong Chen, Jianmin Bao, Fang Wen, Bo Zhang, Dongdong Chen, Lu Yuan, and Baining Guo · 2022
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Cascaded diffusion models for high fidelity image generation
Jonathan Ho, Chitwan Saharia, William Chan, David J Fleet, Mohammad Norouzi, and Tim Salimans · 2022
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
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Neural wavelet-domain diffusion for 3d shape generation
Ka-Hei Hui, Ruihui Li, Jingyu Hu, and Chi-Wing Fu · 2022
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Towards implicit text-guided 3d shape generation
Zhengzhe Liu, Yi Wang, Xiaojuan Qi, and Chi-Wing Fu · 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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Diffusion autoencoders: Toward a meaningful and decodable representation
Konpat Preechakul, Nattanat Chatthee, Suttisak Wizadwongsa, and Supasorn Suwajanakorn · 2022
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Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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Palette: Image-to-image diffusion models
Chitwan Saharia, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi · 2022
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Image super-resolution via iterative refinement
Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J Fleet, and Mohammad Norouzi · 2022
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Face super-resolution using stochastic differential equations
Marcelo dos Santos, Rayson Laroca, Rafael O Ribeiro, João Neves, Hugo Proença, and David Menotti · 2022
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Improved vector quantized diffusion models
Zhicong Tang, Shuyang Gu, Jianmin Bao, Dong Chen, and Fang Wen · 2022
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Shapeformer: Transformer-based shape completion via sparse representation
Xingguang Yan, Liqiang Lin, Niloy J Mitra, Dani Lischinski, Daniel Cohen-Or, and Hui Huang · 2022
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Lion: Latent point diffusion models for 3d shape generation
Xiaohui Zeng, Arash Vahdat, Francis Williams, Zan Gojcic, Or Litany, Sanja Fidler, and Karsten Kreis · 2022
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Seedformer: Patch seeds based point cloud completion with upsample transformer
Haoran Zhou, Yun Cao, Wenqing Chu, Junwei Zhu, Tong Lu, Ying Tai, and Chengjie Wang · 2022
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