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We study the problem of single-image 3D object reconstruction.
Robust Monte Carlo Methods for Light Transport Simulation
Eric Veach · 1997
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Microfacet models for refraction through rough surfaces
Bruce Walter, Stephen R. Marschner, Hongsong Li, and Kenneth E. Torrance · 2007
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Physically-based shading at disney
Brent Burley · 2012
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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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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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Marrnet: 3d shape reconstruction via 2.5 d sketches
Jiajun Wu, Yifan Wang, Tianfan Xue, Xingyuan Sun, Bill Freeman, and Josh Tenenbaum · 2017
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AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation
Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan Russell, and Mathieu Aubry · 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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Learning localized generative models for 3d point clouds via graph convolution
Diego Valsesia, Giulia Fracastoro, and Enrico Magli · 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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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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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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Sagnet: Structure-aware generative network for 3d-shape modeling
Zhijie Wu, Xiang Wang, Di Lin, Dani Lischinski, Daniel Cohen-Or, and Hui Huang · 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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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Progressive point cloud deconvolution generation network
Le Hui, Rui Xu, Jin Xie, Jianjun Qian, and Jian Yang · 2020
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Discrete point flow networks for efficient point cloud generation
Roman Klokov, Edmond Boyer, and Jakob Verbeek · 2020
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Neural-pil: Neural pre-integrated lighting for reflectance decomposition
Mark Boss, Varun Jampani, Raphael Braun, Ce Liu, Jonathan T. Barron, and Hendrik P.A. Lensch · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 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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Common objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction
Jeremy Reizenstein, Roman Shapovalov, Philipp Henzler, Luca Sbordone, Patrick Labatut, and David Novotny · 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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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
Cited alongside, same era.
Shapeclipper: Scalable 3d shape learning from single-view images via geometric and clip-based consistency
Zixuan Huang, Varun Jampani, Anh Thai, Yuanzhen Li, Stefan Stojanov, and James M Rehg · 2023
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Shap-e: Generating conditional 3d implicit functions
Heewoo Jun and Alex Nichol · 2023
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Diffusion-sdf: Text-to-shape via voxelized diffusion
Muheng Li, Yueqi Duan, Jie Zhou, and Jiwen Lu · 2023
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Realfusion: 360deg reconstruction of any object from a single image
Luke Melas-Kyriazi, Iro Laina, Christian Rupprecht, and Andrea Vedaldi · 2023
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Dinov2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al · 2023
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Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi · 2022
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Google scanned objects: A high-quality dataset of 3d scanned household items
Laura Downs, Anthony Francis, Nate Koenig, Brandon Kinman, Ryan Hickman, Krista Reymann, Thomas B McHugh, and Vincent Vanhoucke · 2022
Cited alongside, same era.
Get3d: A generative model of high quality 3d textured shapes learned from images
Jun Gao, Tianchang Shen, Zian Wang, Wenzheng Chen, Kangxue Yin, Daiqing Li, Or Litany, Zan Gojcic, and Sanja Fidler · 2022
Cited alongside, same era.
Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and Denoising
Jon Hasselgren, Nikolai Hofmann, and Jacob Munkberg · 2022
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
Cited alongside, same era.
Autosdf: Shape priors for 3d completion, reconstruction and generation
Paritosh Mittal, Yen-Chi Cheng, Maneesh Singh, and Shubham Tulsiani · 2022
Cited alongside, same era.
Point-e: A system for generating 3d point clouds from complex prompts
Alex Nichol, Heewoo Jun, Prafulla Dhariwal, Pamela Mishkin, and Mark Chen · 2022
Cited alongside, same era.
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Dreamfusion: Text-to-3d using 2d diffusion
Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall · 2023
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Diffusion-based signed distance fields for 3d shape generation
Jaehyeok Shim, Changwoo Kang, and Kyungdon Joo · 2023
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3d neural field generation using triplane diffusion
J Ryan Shue, Eric Ryan Chan, Ryan Po, Zachary Ankner, Jiajun Wu, and Gordon Wetzstein · 2023
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Alpha-clip: A clip model focusing on wherever you want, 2023
Zeyi Sun, Ye Fang, Tong Wu, Pan Zhang, Yuhang Zang, Shu Kong, Yuanjun Xiong, Dahua Lin, and Jiaqi Wang · 2023
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Make-it-3d: High-fidelity 3d creation from a single image with diffusion prior
Junshu Tang, Tengfei Wang, Bo Zhang, Ting Zhang, Ran Yi, Lizhuang Ma, and Dong Chen · 2023
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Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation
Haochen Wang, Xiaodan Du, Jiahao Li, Raymond A Yeh, and Greg Shakhnarovich · 2023
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Michelangelo: Conditional 3d shape generation based on shape-image-text aligned latent representation
Zibo Zhao, Wen Liu, Xin Chen, Xianfang Zeng, Rui Wang, Pei Cheng, BIN FU, Tao Chen, Gang YU, and Shenghua Gao · 2023
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Sf3d: Stable fast 3d mesh reconstruction with uv-unwrapping and illumination disentanglement
Mark Boss, Zixuan Huang, Aaryaman Vasishta, and Varun Jampani · 2024
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Ln3diff: Scalable latent neural fields diffusion for speedy 3d generation
Yushi Lan, Fangzhou Hong, Shuai Yang, Shangchen Zhou, Xuyi Meng, Bo Dai, Xingang Pan, and Chen Change Loy · 2024
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Lgm: Large multi-view gaussian model for high-resolution 3d content creation
Jiaxiang Tang, Zhaoxi Chen, Xiaokang Chen, Tengfei Wang, Gang Zeng, and Ziwei Liu · 2024
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Triposr: Fast 3d object reconstruction from a single image
Dmitry Tochilkin, David Pankratz, Zexiang Liu, Zixuan Huang, , Adam Letts, Yangguang Li, Ding Liang, Christian Laforte, Varun Jampani, and Yan-Pei Cao · 2024
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Crm: Single image to 3d textured mesh with convolutional reconstruction model
Zhengyi Wang, Yikai Wang, Yifei Chen, Chendong Xiang, Shuo Chen, Dajiang Yu, Chongxuan Li, Hang Su, and Jun Zhu · 2024
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Jiale Xu, Weihao Cheng, Yiming Gao, Xintao Wang, Shenghua Gao, and Ying Shan · 2024
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Mosaic-sdf for 3d generative models
Lior Yariv, Omri Puny, Oran Gafni, and Yaron Lipman · 2024
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Gs-lrm: Large reconstruction model for 3d gaussian splatting
Kai Zhang, Sai Bi, Hao Tan, Yuanbo Xiangli, Nanxuan Zhao, Kalyan Sunkavalli, and Zexiang Xu · 2024
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