A collection of resources on neural rendering.
If you think I have missed out on something (or) have any suggestions (papers, implementations and other resources), feel free to pull a request
Feedback and contributions are welcome!
markdown format:
**Here is the Paper Name.**<br>
*[Author 1](homepage), Author 2, and Author 3.*<br>
Conference or Journal Year. [[PDF](link)] [[Project](link)] [[Github](link)] [[Video](link)] [[Data](link)]- Intruduction of Neural Rendering
- Related Surveys and Course Notes
- Inverse Rendering
- Neural Rerendering
- Differentiable Rendering
- Volumetric Performance Capture (Free Viewpoint Video)
- Semantic Photo Synthesis and Manipulation
- Texture and Surface Embedding or Mapping
- Neural Scene Representation and Rendering
- Novel-View Synthesis for Objects and Scenes
- Light, Reflectance, Illuminance and Shade
- Dubbing and Talking-Head Animation
- Motion Transfer, Retargeting, and Reenactment
Neural Rendering is a new and rapidly emerging field that combines generative machine learning techniques with physical knowledge from computer graphics, e.g., by the integration of differentiable rendering into network training.
Ayush Tewari et. al. define Neural Rendering as
Deep image or video generation approaches that enable explicit or implicit control of scene properties such as illumination, camera parameters, pose, geometry, appearance, and semantic structure.
A typical neural rendering approach takes as input images corresponding to certain scene conditions (for example, viewpoint, lighting, layout, etc.), builds a “neural” scene representation from them, and “renders” this representation under novel scene properties to synthesize novel images.
CVPR 2020 tutorial define Neural Rendering as
Neural rendering is a new class of deep image and video generation approaches that enable explicit or implicit control of scene properties such as illumination, camera parameters, pose, geometry, appearance, and semantic structure. It combines generative machine learning techniques with physical knowledge from computer graphics to obtain controllable and photo-realistic outputs.
Given high-quality scene specifications, Classic Rendering Methods can render photorealistic images for a variety of complex real-world phenomena. Moreover, rendering gives us explicit editing control over all the elements of the scene-camera viewpoint, lighting, geometry and materials. However, building high-quality scene models, especially directly from images, requires significant manual effort, and automated scene modeling from images is an open research problem. On the other hand, Deep Generative Networks are now starting to produce visually compelling images and videos either from random noise, or conditioned on certain user specifications like scene segmentation and layout. However, they do not yet allow for fine-grained control over scene appearance and cannot always handle the complex, non-local, 3D interactions between scene properties. In contrast, neural rendering methods hold the promise of combining these approaches to enable controllable, high-quality synthesis of novel images from input images/videos.
State of the Art on Neural Rendering.
Ayush Tewari, Ohad Fried, Justus Thies, Vincent Sitzmann, Stephen Lombardi, Kalyan Sunkavalli, Ricardo Martin-Brualla, Tomas Simon, Jason Saragih, Matthias Nießner, Rohit Pandey, Sean Fanello, Gordon Wetzstein, Jun-Yan Zhu, Christian Theobalt, Maneesh Agrawala, Eli Shechtman, Dan B Goldman, Michael Zollhöfer.
Eurographics 2020.
CVPR 2020 tutorial on Neural Rendering.
Ayush Tewari, Ohad Fried, Justus Thies, Vincent Sitzmann, Stephen Lombardi, Kalyan Sunkavalli, Ricardo Martin-Brualla, Tomas Simon, Jason Saragih, Matthias Nießner, Rohit K. Pandey, Sean Fanello, Gordon Wetzstein, Jun-Yan Zhu, Christian Theobalt, Maneesh Agrawala, Eli Shechtman, Dan B. Goldman, Michael Zollhöfer.
Differentiable Rendering: A Survey.
Hiroharu Kato, Deniz Beker, Mihai Morariu, Takahiro Ando, Toru Matsuoka, Wadim Kehl, Adrien Gaidon.
PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting.
Kai Zhang, Fujun Luan, Qianqian Wang, Kavita Bala, Noah Snavely.
CVPR 2021. [PDF] [Project]
Invertible Neural BRDF for Object Inverse Rendering.
Zhe Chen, Shohei Nobuhara, Ko Nishino.
ECCV 2020. [PDF]
Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF From a Single Image.
Zhengqin Li, Mohammad Shafiei, Ravi Ramamoorthi, Kalyan Sunkavalli, Manmohan Chandraker.
CVPR 2020.[PDF] [Project] [Github]
Polarimetric Multi-View Inverse Rendering.
Jinyu Zhao, Yusuke Monno, Masatoshi Okutomi.
ECCV 2020. [PDF]
NiLBS: Neural Inverse Linear Blend Skinning.
Timothy Jeruzalski, David I.W. Levin, Alec Jacobson, Paul Lalonde, Mohammad Norouzi, Andrea Tagliasacchi.
arxiv 2020. [PDF]
Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer.
Wenzheng Chen, Jun Gao, Huan Ling, Edward J. Smith, Jaakko Lehtinen, Alec Jacobson, Sanja Fidler.
NeurIPS 2019. [PDF]
DRWR: A Differentiable Renderer without Rendering for Unsupervised 3D Structure Learning from Silhouette Images.
Zhizhong Han, Chao Chen, Yu-Shen Liu, Matthias Zwicker.
ICML 2020. [PDF]
InverseRenderNet: Learning Single Image Inverse Rendering.
Ye Yu, William A. P. Smith.
CVPR 2019. [PDF] [Github] [IIW Dataset]
Learning Inverse Rendering of Faces from Real-world Videos.
Yuda Qiu, Zhangyang Xiong, Kai Han, Zhongyuan Wang, Zixiang Xiong, Xiaoguang Han.
arxiv, 2020. [PDF] [Github]
Neural Lumigraph Rendering.
Petr Kellnhofer, Lars Jebe, Andrew Jones, Ryan Spicer, Kari Pulli, Gordon Wetzstein.
CVPR 2021. [PDF] [Project] [Data]
NeRViS: Neural Re-rendering for Full-frame Video Stabilization.
Yu-Lun Liu, Wei-Sheng Lai, Ming-Hsuan Yang, Yung-Yu Chuang, Jia-Bin Huang.
arxiv 2021. [PDF] [Github]
Neural Re-Rendering of Humans from a Single Image.
Kripasindhu Sarkar, Dushyant Mehta, Weipeng Xu, Vladislav Golyanik, Christian Theobalt.
ECCV 2020. [PDF]
StyleUV: Diverse and High-fidelity UV Map Generative Model.
Myunggi Lee, Wonwoong Cho, Moonheum Kim, David Inouye, Nojun Kwak.
arxiv 2020. [PDF]
Neural Rerendering in the Wild.
Moustafa Meshry, Dan B Goldman, Sameh Khamis, Hugues Hoppe, Rohit Pandey, Noah Snavely, Ricardo Martin-Brualla.
CVPR 2019. [PDF]
Revealing Scenes by Inverting Structure from Motion Reconstructions.
Francesco Pittaluga, Sanjeev J. Koppal, Sing Bing Kang, Sudipta N. Sinha.
CVPR 2019. [PDF]
Neural Re-Rendering of Humans from a Single Image.
Kripasindhu Sarkar, Dushyant Mehta, Weipeng Xu, Vladislav Golyanik, Christian Theobalt.
ECCV 2020. [PDF]
Modular Primitives for High-Performance Differentiable Rendering.
Samuli Laine, Janne Hellsten, Tero Karras, Yeongho Seol, Jaakko Lehtinen, Timo Aila.
arxiv 2020. [PDF] [Github]
Monocular Differentiable Rendering for Self-Supervised 3D Object Detection.
Deniz Beker, Hiroharu Kato, Mihai Adrian Morariu, Takahiro Ando, Toru Matsuoka, Wadim Kehl, Adrien Gaidon.
ECCV 2020. [PDF]
POSEFusion: Pose-guided Selective Fusion for Single-view Human Volumetric Capture.
Zhe Li, Tao Yu, Zerong Zheng, Kaiwen Guo, Yebin Liu.
CVPR 2021 (oral). [PDF] [Github]
Monocular Real-Time Volumetric Performance Capture.
Ruilong Li, Yuliang Xiu, Shunsuke Saito, Zeng Huang, Kyle Olszewski, Hao Li.
arxiv 2020. [PDF]
Neural Sparse Voxel Fields.
Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, Christian Theobalt.
arxiv 2020. [PDF]
Volumetric Capture of Humans with a Single RGBD Camera via Semi-Parametric Learning.
Rohit Pandey, Anastasia Tkach, Shuoran Yang, Pavel Pidlypenskyi, Jonathan Taylor, Ricardo Martin-Brualla, Andrea Tagliasacchi, George Papandreou, Philip Davidson, Cem Keskin, Shahram Izadi, Sean Fanello.
CVPR 2019. [PDF]
Neural Volumes: Learning Dynamic Renderable Volumes from Images.
Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, Yaser Sheikh.
SIGGRAPH 2019. [PDF]
StyleFlow: Attribute-conditioned Exploration of StyleGAN-Generated Images using Conditional Continuous Normalizing Flows.
Rameen Abdal, Peihao Zhu, Niloy Mitra, Peter Wonka.
Siggraph Asia 2020. [PDF] [Github]
Layered Neural Rendering for Retiming People in Video.
Erika Lu, Forrester Cole, Tali Dekel, Weidi Xie, Andrew Zisserman, David Salesin, William T. Freeman, Michael Rubinstein.
SIGGRAPH Asia 2020. [PDF] [Project]
Neural Hair Rendering.
Menglei Chai, Jian Ren, Sergey Tulyakov.
arxiv 2020. [PDF]
MichiGAN: Multi-Input-Conditioned Hair Image Generation for Portrait Editing.
Zhentao Tan, Menglei Chai, Dongdong Chen, Jing Liao, Qi Chu, Lu Yuan, Sergey Tulyakov, Nenghai Yu.
SIGGRAPH 2020. [PDF]
pix2pixHD: High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs.
Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Andrew Tao, Jan Kautz, Bryan Catanzaro.
CVPR 2018. [PDF] [Github]
SPADE: Semantic Image Synthesis with Spatially-Adaptive Normalization.
Taesung Park, Ming-Yu Liu, Ting-Chun Wang, Jun-Yan Zhu.
CVPR 2019. [PDF] [Github]
Semantic Bottleneck Scene Generation.
Samaneh Azadi, Michael Tschannen, Eric Tzeng, Sylvain Gelly, Trevor Darrell, Mario Lucic.
arxiv, 2019. [PDF]
Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation.
Hao Tang, Dan Xu, Yan Yan, Philip H. S. Torr, Nicu Sebe.
CVPR 2020. [PDF] [Github]
SelectionGAN: Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation.
Hao Tang, Dan Xu, Nicu Sebe, Yanzhi Wang, Jason J. Corso, Yan Yan.
VPR 2019. [PDF] [Github]
Neural Surface Maps.
Luca Morreale, Noam Aigerman, Vladimir Kim, Niloy J. Mitra.
arxiv 2021. [PDF] [Github]
NeuTex: Neural Texture Mapping for Volumetric Neural Rendering.
Fanbo Xiang, Zexiang Xu, Miloš Hašan, Yannick Hold-Geoffroy, Kalyan Sunkavalli, Hao Su.
arxiv 2021. [PDF]
Better Patch Stitching for Parametric Surface Reconstruction.
Zhantao Deng, Jan Bednařík, Mathieu Salzmann, Pascal Fua.
3DV 2020. [PDF]
Continuous Surface Embeddings.
Natalia Neverova, David Novotny, Marc Szafraniec, Vasil Khalidov, Patrick Labatut, Andrea Vedaldi.
NerIPS 2020. [PDF]
Transposer: Universal Texture Synthesis Using Feature Maps as Transposed Convolution Filter.
Guilin Liu, Rohan Taori, Ting-Chun Wang, Zhiding Yu, Shiqiu Liu, Fitsum A. Reda, Karan Sapra, Andrew Tao, Bryan Catanzaro.
arxiv 2020. [PDF]
Wasserstein Generative Models for Patch-based Texture Synthesis.
Antoine Houdard, Arthur Leclaire, Nicolas Papadakis, Julien Rabin.
arxiv 2020. [PDF]
Deep Geometric Texture Synthesis.
Amir Hertz, Rana Hanocka, Raja Giryes, Daniel Cohen-Or.
SIGGRAPH 2020. [PDF]
GramGAN: Deep 3D Texture Synthesis From 2D Exemplars.
Tiziano Portenier, Siavash Bigdeli, Orçun Göksel.
arxiv 2020. [PDF]
GPU-Accelerated Mobile Multi-view Style Transfer.
Puneet Kohli, Saravana Gunaseelan, Jason Orozco, Yiwen Hua, Edward Li, Nicolas Dahlquist.
arxiv 2020. [PDF]
Leveraging 2D Data to Learn Textured 3D Mesh Generation.
Paul Henderson, Vagia Tsiminaki, Christoph H. Lampert.
CVPR 2020. [PDF]
Articulation-aware Canonical Surface Mapping.
Nilesh Kulkarni, Abhinav Gupta, David F. Fouhey, Shubham Tulsiani.
CVPR 2020. [PDF] [Github] [Project]
UnrealText: Synthesizing Realistic Scene Text Images from the Unreal World.
Shangbang Long, Cong Yao.
CVPR 2020. [PDF] [Github]
Adversarial Texture Optimization from RGB-D Scans.
Jingwei Huang, Justus Thies, Angela Dai, Abhijit Kundu, Chiyu Jiang, Leonidas Guibas, Matthias Nießner, Thomas Funkhouser.
CVPR 2020. [PDF] [Project] [Github] [pyRender]
CSM: Canonical Surface Mapping via Geometric Cycle Consistency.
Nilesh Kulkarni, Abhinav Gupta, Shubham Tulsiani.
ICCV 2019. [PDF] [Github] [Project]
Texture Mapping for 3D Reconstruction with RGB-D Sensor.
Yanping Fu, Qingan Yan, Long Yang, Jie Liao, Chunxia Xiao.
CVPR 2018. [PDF] [thecvf] [Code on Github]
Let There Be Color! - Large-Scale Texturing of 3D Reconstructions.
Waechter, Michael and Moehrle, Nils and Goesele, Michael.
ECCV 2018. [PDF] [Project] [Github] [rayint] [Eigen] [Multi-View Environment] [mapMAP]
Learning Category-Specific Mesh Reconstruction from Image Collections.
Angjoo Kanazawa, Shubham Tulsiani Alexei A. Efros, Jitendra Malik.
ECCV 2018. [Github] [Project]
Texture Fields: Learning Texture Representations in Function Space.
Michael Oechsle, Lars Mescheder, Michael Niemeyer, Thilo Strauss, Andreas Geiger.
ICCV 2019. [PDF]
AtlasNet: A Papier-Mache Approach to Learning 3D Surface Generation.
Thibault Groueix, Matthew Fisher, Vladimir Kim, Bryan Russell, Mathieu Aubry.
CVPR 2018. [PDF] [Project] [Github]
Learning Elementary Structures For 3D Shape Generation And Matching.
Theo Deprelle, Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, Mathieu Aubry.
arxiv, 2019. [PDF]
[Project]
[Github]
Learning to Generate Textures on Meshes.
Amit Raj, Cusuh Ham, Connelly Barnes, Vladimir Kim, Jingwan Lu, James Hays.
CVPR Deep Generative Models for 3D Understanding 2019 (Best Paper). [PDF]
Unsupervised Texture Transfer from Images to Model Collections.
Tuanfeng Yand Wang, Hao Su, Qixing Huang, Jingwei Huang, Leonidas J. Guibas, Niloy J. Mitra.
SIGGRAPH Asia 2016. [PDF]
[Project]
[Data]
[Awesome Neural Radiance Fields]
UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction.
Michael Oechsle, Songyou Peng, Andreas Geiger.
arxiv 2021. [PDF]
BARF: Bundle-Adjusting Neural Radiance Fields.
Chen-Hsuan Lin, Wei-Chiu Ma, Antonio Torralba, Simon Lucey.
arxiv 2021. [PDF]
SNARF: Differentiable Forward Skinning for Animating Non-Rigid Neural Implicit Shapes.
Xu Chen, Yufeng Zheng, Michael J. Black, Otmar Hilliges, Andreas Geiger.
CVPR 2021. [PDF]
Convolutional Neural Opacity Radiance Fields.
Haimin Luo, Anpei Chen, Qixuan Zhang, Bai Pang, Minye Wu, Lan Xu, Jingyi Yu.
arxiv 2021. [PDF]
KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs.
Christian Reiser, Songyou Peng, Yiyi Liao, Andreas Geiger.
arxiv 2021. [PDF]
MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereo.
Anpei Chen, Zexiang Xu, Fuqiang Zhao, Xiaoshuai Zhang, Fanbo Xiang, Jingyi Yu, Hao Su.
arxiv 2021. [PDF]
PlenOctrees for Real-time Rendering of Neural Radiance Fields.
Alex Yu, Ruilong Li, Matthew Tancik, Hao Li, Ren Ng, Angjoo Kanazawa.
arxiv 2021. [PDF]
FastNeRF: High-Fidelity Neural Rendering at 200FPS.
Stephan J. Garbin, Marek Kowalski, Matthew Johnson, Jamie Shotton, Julien Valentin.
arxiv 2021. [PDF]
DONeRF: Towards Real-Time Rendering of Neural Radiance Fields using Depth Oracle Networks.
Thomas Neff, Pascal Stadlbauer, Mathias Parger, Andreas Kurz, Chakravarty R. Alla Chaitanya, Anton Kaplanyan, Markus Steinberger.
arxiv 2021. [PDF] [Project]
Mixture of Volumetric Primitives for Efficient Neural Rendering.
Stephen Lombardi, Tomas Simon, Gabriel Schwartz, Michael Zollhoefer, Yaser Sheikh, Jason Saragih.
arxiv 2021. [PDF]
ShaRF: Shape-conditioned Radiance Fields from a Single View.
Konstantinos Rematas, Ricardo Martin-Brualla, Vittorio Ferrari.
arxiv 2021. [PDF] [Project]
NeRF--: Neural Radiance Fields Without Known Camera Parameters.
Zirui Wang, Shangzhe Wu, Weidi Xie, Min Chen, Victor Adrian Prisacariu.
arxiv 2021. [PDF] [Project]
A-NeRF: Surface-free Human 3D Pose Refinement via Neural Rendering.
Shih-Yang Su, Frank Yu, Michael Zollhoefer, Helge Rhodin.
arxiv 2021. [PDF] [Project]
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, Sanja Fidler.
arxiv 2021. [PDF]
Neural Volume Rendering: NeRF And Beyond.
Frank Dellaert, Lin Yen-Chen.
arxiv 2021. [PDF]
Non-line-of-Sight Imaging via Neural Transient Fields.
Siyuan Shen, Zi Wang, Ping Liu, Zhengqing Pan, Ruiqian Li, Tian Gao, Shiying Li, Jingyi Yu.
arxiv 2021. [PDF]
DeepSurfels: Learning Online Appearance Fusion.
Marko Mihajlovic, Silvan Weder, Marc Pollefeys, Martin R. Oswald.
arxiv 2021. [PDF]
Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Deforming Scene from Monocular Video.
Edgar Tretschk, Ayush Tewari, Vladislav Golyanik, Michael Zollhöfer, Christoph Lassner, Christian Theobalt.
arxiv 2020. [PDF] [Project] [Github]
Learned Initializations for Optimizing Coordinate-Based Neural Representations.
Matthew Tancik, [Ben Mildenhall, Terrance Wang, Divi Schmidt, Pratul P. Srinivasan, Jonathan T. Barron, Ren Ng.
arxiv 2020. [PDF] [Project]
Learning Compositional Radiance Fields of Dynamic Human Heads.
Ziyan Wang, Timur Bagautdinov, Stephen Lombardi, Tomas Simon, Jason Saragih, Jessica Hodgins, Michael Zollhöfer.
arxiv 2020. [PDF]
OSF: Object-Centric Neural Scene Rendering.
Michelle Guo, Alireza Fathi, Jiajun Wu, Thomas Funkhouser.
arxiv 2020. [PDF] [Project]
Deformed Implicit Field: Modeling 3D Shapes with Learned Dense Correspondence.
Yu Deng, Jiaolong Yang, Xin Tong.
arxiv 2020. [PDF]
Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid Representations.
Wang Yifan, Shihao Wu, Cengiz Oztireli, Olga Sorkine-Hornung.
arxiv 2020. [PDF]
Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction.
Guy Gafni, Justus Thies, Michael Zollhöfer, Matthias Nießner.
arxiv 2020. [PDF] [Project] [Video]
Portrait Neural Radiance Fields from a Single Image.
Chen Gao, Yichang Shih, Wei-Sheng Lai, Chia-Kai Liang, Jia-Bin Huang.
arxiv 2020. [PDF] [Project]
iNeRF: Inverting Neural Radiance Fields for Pose Estimation.
Lin Yen-Chen, Pete Florence, Jonathan T. Barron, Alberto Rodriguez, Phillip Isola, Tsung-Yi Lin.
arxiv 2020. [PDF] [Project]
NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis.
Pratul P. Srinivasan, Boyang Deng, Xiuming Zhang, Matthew Tancik, Ben Mildenhall, Jonathan T. Barron.
arxiv 2020. [PDF] [Project]
pixelNeRF: Neural Radiance Fields from One or Few Images.
Alex Yu, Vickie Ye, Matthew Tancik, Angjoo Kanazawa.
CVPR 2021. [PDF] [Project]
AutoInt: Automatic Integration for Fast Neural Volume Rendering.
David B. Lindell, Julien N. P. Martel, Gordon Wetzstein.
CVPR 2021 (oral). [PDF] [Project]
Space-time Neural Irradiance Fields for Free-Viewpoint Video.
Wenqi Xian, Jia-Bin Huang, Johannes Kopf, Changil Kim.
arxiv 2020. [PDF] [Project]
DeRF: Decomposed Radiance Fields.
Daniel Rebain, Wei Jiang, Soroosh Yazdani, Ke Li, Kwang Moo Yi, Andrea Tagliasacchi.
CVPR 2021. [PDF] [Project]
D-NERF: Deformable Neural Radiance Fields.
Keunhong Park, Utkarsh Sinha, Jonathan T. Barron, Sofien Bouaziz, Dan B Goldman, Steven M. Seitz, Ricardo-Martin Brualla.
CVPR 2021. [PDF] [Project]
GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields.
Michael Niemeyer, Andreas Geiger.
arxiv 2020. [PDF]
Neural Unsigned Distance Fields for Implicit Function Learning.
Julian Chibane, Aymen Mir, Gerard Pons-Moll.
NeurIPS 2020. [PDF] [Github]
SIREN: Implicit Neural Representations with Periodic Activation Functions.
Vincent Sitzmann, Julien N. P. Martel, Alexander W. Bergman, David B. Lindell, Gordon Wetzstein.
NeurIPS 2020 (Oral). [PDF] [Project] [Github] [Data]
GRF: Learning a General Radiance Field for 3D Scene Representation and Rendering.
Alex Trevithick, Bo Yang.
arxiv 2020. [PDF] [Github]
3D Scene Generation.
Angel X. Chang, Daniel Ritchie, Qixing Huang, Manolis Savva.
CVPR 2019 Workshop.
Local Deep Implicit Functions for 3D Shape.
Kyle Genova, Forrester Cole, Avneesh Sud, Aaron Sarna, Thomas Funkhouser.
CVPR 2020. [PDF]
PatchNets: Patch-Based Generalizable Deep Implicit 3D Shape Representations.
Edgar Tretschk, Ayush Tewari, Vladislav Golyanik, Michael Zollhöfer, Carsten Stoll, Christian Theobalt.
arxiv 2020. [PDF]
Unsupervised 3D Learning for Shape Analysis via Multiresolution Instance Discrimination.
Peng-Shuai Wang, Yu-Qi Yang, Qian-Fang Zou, Zhirong Wu, Yang Liu, Xin Tong.
arxiv 2020. [PDF]
Learning Implicit Fields for Generative Shape Modeling.
Zhiqin Chen, Hao Zhang.
CVPR 2019. [PDF] [Project] [Github]
Geometric Correspondence Fields: Learned Differentiable Rendering for 3D Pose Refinement in the Wild.
Alexander Grabner, Yaming Wang, Peizhao Zhang, Peihong Guo, Tong Xiao, Peter Vajda, Peter M. Roth, Vincent Lepetit.
ECCV 2020. [PDF]
Equivariant Neural Rendering.
Emilien Dupont, Miguel Angel Bautista, Alex Colburn, Aditya Sankar, Carlos Guestrin, Josh Susskind, Qi Shan.
ICML 2020. [PDF] [Github]
CoReNet: Coherent 3D Scene Reconstruction From a Single RGB Image.
Stefan Popov, Pablo Bauszat, Vittorio Ferrari.
arxiv 2020. [PDF]
Single-View View Synthesis with Multiplane Images.
Richard Tucker and Noah Snavely.
CVPR 2020. [PDF] [Project]
LIMP: Learning Latent Shape Representations with Metric Preservation Priors.
Luca Cosmo, Antonio Norelli, Oshri Halimi, Ron Kimmel, Emanuele Rodolà.
arxiv 2020. [PDF]
Learning 3D Part Assembly from a Single Image.
Yichen Li, Kaichun Mo, Lin Shao, Minhyuk Sung, Leonidas Guibas.
arxiv 2020. [PDF]
Curriculum DeepSDF.
Yueqi Duan, Haidong Zhu, He Wang, Li Yi, Ram Nevatia, Leonidas J. Guibas.
arxiv, 2020. [PDF] [Github]
PolyGen: An Autoregressive Generative Model of 3D Meshes.
Charlie Nash, Yaroslav Ganin, S. M. Ali Eslami, Peter W. Battaglia.
arxiv, 2020. [PDF]
Self-supervised Learning of 3D Objects from Natural Images.
Hiroharu Kato, Tatsuya Harada.
arxiv, 2019. [PDF] [Project]
BlockGAN: Learning 3D Object-Aware Scene Representations from Unlabelled Images.
Thu Nguyen-Phuoc, Christian Richardt, Long Mai, Yong-Liang Yang, Niloy Mitra.
arxiv, 2020. [PDF] [Project]
DualSDF: Semantic Shape Manipulation using a Two-Level Representation.
Zekun Hao, Hadar Averbuch-Elor, Noah Snavely, Serge Belongie.
CVPR 2020. [PDF]
Learning a Neural 3D Texture Space from 2D Exemplars.
Philipp Henzler, Niloy J. Mitra, Tobias Ritschel.
CVPR 2020. [PDF] [Project]
Neural Contours: Learning to Draw Lines from 3D Shapes.
Difan Liu, Mohamed Nabail, Aaron Hertzmann, Evangelos Kalogerakis.
CVPR 2020. [PDF] [Github]
Pix2Shape: Towards Unsupervised Learning of 3D Scenes from Images using a View-based Representation.
Sai Rajeswar, Fahim Mannan, Florian Golemo, Jérôme Parent-Lévesque, David Vazquez, Derek Nowrouzezahrai, Aaron Courville.
IJCV 2020. [PDF]
VCN: Volumetric Correspondence Networks for Optical Flow.
Gengshan Yang, Deva Ramanan.
NeurIPS 2019. [PDF] [GitHub] [Project]
Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations.
Vincent Sitzmann, Michael Zollhöfer, Gordon Wetzstein.
NeurIPS 2019 (Oral, Honorable Mention "Outstanding New Directions").
[PDF] [Project] [Github] [Dataset]
Transformable Bottleneck Networks.
Kyle Olszewski, Sergey Tulyakov, Oliver Woodford, Hao Li, Linjie Luo.
ICCV 2019. [PDF]
Equivariant Multi-View Networks.
Carlos Esteves, Yinshuang Xu, Christine Allen-Blanchette, Kostas Daniilidis.
ICCV 2019. [PDF]
DeepVoxels: Learning Persistent 3D Feature Embeddings.
Vincent Sitzmann, Justus Thies, Felix Heide, Matthias Nießner, Gordon Wetzstein, Michael Zollhöfer.
CVPR 2019 (Oral). [Project] [PDF] [Code]
Occupancy Networks: Learning 3D Reconstruction in Function Space.
Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, Andreas Geiger.
CVPR 2019. [PDF] [Github]
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation.
eong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, Steven Lovegrove.
CVPR 2019. [PDF] [Github]
DeepSDF x Sim(3): Extending DeepSDF for automatic 3D shape retrieval and similarity transform estimation.
Oladapo Afolabi, Allen Yang, Shankar S. Sastry.
arxiv 2020. [PDF]
Learning View Priors for Single-view 3D Reconstruction.
Hiroharu Kato, Tatsuya Harada.
CVPR 2019. [PDF] [Project] [Github]
HoloGAN: Unsupervised Learning of 3D Representations from Natural Images.
Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt Yong-liang Yang.
ICCV 2019. [PDF] [GitHub]
C3DPO: Canonical 3D Pose Networks for Non-Rigid Structure From Motion.
David Novotny, Nikhila Ravi, Benjamin Graham, Natalia Neverova, Andrea Vedaldi.
ICCV 2019. [PDF] [Github] [Project]
CSM: Canonical Surface Mapping via Geometric Cycle Consistency.
Nilesh Kulkarni, Abhinav Gupta, Shubham Tulsiani.
ICCV 2019. [PDF] [Github] [Project]
Neural Scene Graphs for Dynamic Scenes.
Julian Ost, Fahim Mannan, Nils Thuerey, Julian Knodt, Felix Heide.
CVPR 2021. [PDF] [Project]
Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenes.
Julian Chibane, Aayush Bansal, Verica Lazova, Gerard Pons-Moll.
CVPR 2021. [PDF]
Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis.
Ajay Jain, Matthew Tancik, Pieter Abbeel.
arxiv 2021. [PDF] [Project]
Baking Neural Radiance Fields for Real-Time View Synthesis.
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