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We propose im2nerf, a learning framework that predicts a continuous neural object representation given a single input image in the wild, supervised by only segmentation output from off-the-shelf recognition methods.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Beyond pascal: A benchmark for 3d object detection in the wild
Yu Xiang, Roozbeh Mottaghi, and Silvio Savarese · 2014
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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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Synthesizing training images for boosting human 3d pose estimation
Wenzheng Chen, Huan Wang, Yangyan Li, Hao Su, Zhenhua Wang, Changhe Tu, Dani Lischinski, Daniel Cohen-Or, and Baoquan Chen · 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, William T Freeman, and Joshua B Tenenbaum · 2016
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3d shape induction from 2d views of multiple objects
Matheus Gadelha, Subhransu Maji, and Rui Wang · 2017
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Semantic scene completion from a single depth image
Shuran Song, Fisher Yu, Andy Zeng, Angel X. Chang, Manolis Savva, and Thomas Funkhouser · 2017
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Learning from synthetic humans
Gül Varol, Javier Romero, Xavier Martin, Naureen Mahmood, Michael J. Black, Ivan Laptev, and Cordelia Schmid · 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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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, Fredo Durand, and Jaakko Lehtinen · 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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Factoring shape, pose, and layout from the 2d image of a 3d scene
Shubham Tulsiani, Saurabh Gupta, David Fouhey, Alexei A. Efros, and Jitendra Malik · 2018
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Large-scale interactive object segmentation with human annotators
Rodrigo Benenson, Stefan Popov, and Vittorio Ferrari · 2019
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Mesh r-cnn
Georgia Gkioxari, Jitendra Malik, and Justin Johnson · 2019
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Soft rasterizer: A differentiable renderer for image-based 3d reasoning
Shichen Liu, Tianye Li, Weikai Chen, , and Hao Li · 2019
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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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Hologan: Unsupervised learning of 3d representations from natural images
Thu Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt, and Yong-Liang Yang · 2019
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DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
Jeong Joon Park, Pete Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove · 2019
Cited alongside, same era.
Three-d safari: Learning to estimate zebra pose, shape, and texture from images ”in the wild”
Silvia Zuffi, Angjoo Kanazawa, Tanya Berger-Wolf, and Michael J. Black · 2019
Cited alongside, same era.
Shape and viewpoint without keypoints
Shubham Goel, Angjoo Kanazawa, and Jitendra Malik · 2020
Cited alongside, same era.
Sdfdiff: Differentiable rendering of signed distance fields for 3d shape optimization
Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis
Ajay Jain, Matthew Tancik, and Pieter Abbeel · 2021
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CodeNeRF: Disentangled Neural Radiance Fields for Object Categories
Wongbong Jang and Lourdes Agapito · 2021
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NeRF-VAE: A Geometry Aware 3D Scene Generative Model
Adam Kosiorek, Heiko Strathmann, Daniel Zoran, Pol Moreno, Rosalia Schneider, Sona Mokrá, and Danilo Rezende · 2021
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Tianye Li, Mira Slavcheva, Michael Zollhoefer, Simon Green, Christoph Lassner, Changil Kim, Tanner Schmidt, Steven Lovegrove, Michael Goesele, and Zhaoyang Lv · 2021
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BARF: Bundle-Adjusting Neural Radiance Fields
Chen-Hsuan Lin, Wei-Chiu Ma, Antonio Torralba, and Simon Lucey · 2021
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Yue Jiang, Dantong Ji, Zhizhong Han, and Matthias Zwicker · 2020
Cited alongside, same era.
The open images dataset v4
Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Alexander Kolesnikov, et al · 2020
Cited alongside, same era.
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
Ben Mildenhall, Pratul Srinivasan, Matthew Tancik, Jonathan Barron, Ravi Ramamoorthi, and Ren Ng · 2020
Cited alongside, same era.
Dops: Learning to detect 3d objects and predict their 3d shapes
Mahyar Najibi, Guangda Lai, Abhijit Kundu, Zhichao Lu, Vivek Rathod, Thomas Funkhouser, Caroline Pantofaru, David Ross, Larry S. Davis, and Alireza Fathi · 2020
Cited alongside, same era.
Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision
Michael Niemeyer, Lars Mescheder, Michael Oechsle, and Andreas Geiger · 2020
Cited alongside, same era.
Neural Voxel Renderer: Learning an Accurate and Controllable Rendering Tool
Konstantinos Rematas and Vittorio Ferrari · 2020
Cited alongside, same era.
GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis
Katja Schwarz, Yiyi Liao, Michael Niemeyer, and Andreas Geiger · 2020
Cited alongside, same era.
NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections
Ricardo Martin-Brualla, Noha Radwan, Mehdi Sajjadi, Jonathan Barron, Alexey Dosovitskiy, and Daniel Duckworth · 2021
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Gnerf: Gan-based neural radiance field without posed camera
Quan Meng, Anpei Chen, Haimin Luo, Minye Wu, Hao Su, Lan Xu, Xuming He, and Jingyi Yu · 2021
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GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields
Michael Niemeyer and Andreas Geiger · 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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Decomposing 3D Scenes into Objects via Unsupervised Volume Segmentation
Karl Stelzner, Kristian Kersting, and Adam Kosiorek · 2021
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GRF: Learning a General Radiance Field for 3D Scene Representation and Rendering
Alex Trevithick and Bo Yang · 2021
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IBRNet: Learning Multi-View Image-Based Rendering
Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul Srinivasan, Howard Zhou, Jonathan Barron, Ricardo Martin-Brualla, Noah Snavely, and Thomas Funkhouser · 2021
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NeRF–: Neural Radiance Fields Without Known Camera Parameters
Zirui Wang, Shangzhe Wu, Weidi Xie, Min Chen, and Victor Adrian Prisacariu · 2021
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Shelf-supervised mesh prediction in the wild
Yufei Ye, Shubham Tulsiani, and Abhinav Gupta · 2021
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iNeRF: Inverting Neural Radiance Fields for Pose Estimation
Lin Yen-Chen, Pete Florence, Jonathan Barron, Alberto Rodriguez, Phillip Isola, and Tsung-Yi Lin · 2021
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pixelNeRF: Neural Radiance Fields from One or Few Images
Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa · 2021
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In-Place Scene Labelling and Understanding with Implicit Scene Representation
Shuaifeng Zhi, Tristan Laidlow, Stefan Leutenegger, and Andrew Davison · 2021
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