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We present a method to map 2D image observations of a scene to a persistent 3D scene representation, enabling novel view synthesis and disentangled representation of the movable and immovable components of the scene.
Scikit-learn: Machine learning in python
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Nishchal Bhandari · 2018
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Ricson Cheng, Ziyan Wang, and Katerina Fragkiadaki · 2018
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Adam Kosiorek, Hyunjik Kim, Yee Whye Teh, and Ingmar Posner · 2018
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Christopher P Burgess, Loic Matthey, Nicholas Watters, Rishabh Kabra, Irina Higgins, Matt Botvinick, and Alexander Lerchner · 2019
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Eric Crawford and Joelle Pineau · 2019
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Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Christopher Burgess, Daniel Zoran, Loic Matthey, Matthew Botvinick, and Alexander Lerchner · 2019
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Jindong Jiang, Sepehr Janghorbani, Gerard de Melo, and Sungjin Ahn · 2019
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Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh · 2019
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Thomas Roddick, Alex Kendall, and Roberto Cipolla · 2019
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Scene representation networks: Continuous 3d-structure-aware neural scene representations
Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein · 2019
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Learning spatial common sense with geometry-aware recurrent networks
Hsiao-Yu Fish Tung, Ricson Cheng, and Katerina Fragkiadaki · 2019
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Genesis: Generative scene inference and sampling with object-centric latent representations
Martin Engelcke, Adam R. Kosiorek, Oiwi Parker Jones, and Ingmar Posner · 2020
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Object-centric neural scene rendering
Michelle Guo, Alireza Fathi, Jiajun Wu, and Thomas Funkhouser · 2020
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Zhewei Huang, Tianyuan Zhang, Wen Heng, Boxin Shi, and Shuchang Zhou · 2020
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Space: Unsupervised object-oriented scene representation via spatial attention and decomposition
Zhixuan Lin, Yi-Fu Wu, Skand Vishwanath Peri, Weihao Sun, Gautam Singh, Fei Deng, Jindong Jiang, and Sungjin Ahn · 2020
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Object-centric learning with slot attention
Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf · 2020
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Kaustubh Mani, Swapnil Daga, Shubhika Garg, Sai Shankar Narasimhan, Madhava Krishna, and Krishna Murthy Jatavallabhula · 2020
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Nerf: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng · 2020
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Blockgan: Learning 3d object-aware scene representations from unlabelled images
Thu H Nguyen-Phuoc, Christian Richardt, Long Mai, Yongliang Yang, and Niloy Mitra · 2020
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Donerf: Towards real-time rendering of compact neural radiance fields using depth oracle networks
Thomas Neff, Pascal Stadlbauer, Mathias Parger, Andreas Kurz, Joerg H Mueller, Chakravarty R Alla Chaitanya, Anton Kaplanyan, and Markus Steinberger · 2021
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Giraffe: Representing scenes as compositional generative neural feature fields
Michael Niemeyer and Andreas Geiger · 2021
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Neural scene graphs for dynamic scenes
Julian Ost, Fahim Mannan, Nils Thuerey, Julian Knodt, and Felix Heide · 2021
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Scene representation transformer: Geometry-free novel view synthesis through set-latent scene representations
Mehdi SM Sajjadi, Henning Meyer, Etienne Pot, Urs Bergmann, Klaus Greff, Noha Radwan, Suhani Vora, Mario Lucic, Daniel Duckworth, Alexey Dosovitskiy, et al · 2021
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Light field networks: Neural scene representations with single-evaluation rendering
Vincent Sitzmann, Semon Rezchikov, William T. Freeman, Joshua B. Tenenbaum, and Fredo Durand · 2021
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Grf: Learning a general radiance field for 3d representation and rendering
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Neuraldiff: Segmenting 3d objects that move in egocentric videos
Vadim Tschernezki, Diane Larlus, and Andrea Vedaldi · 2021
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Star: Self-supervised tracking and reconstruction of rigid objects in motion with neural rendering
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Single-shot scene reconstruction
Sergey Zakharov, Rares Andrei Ambrus, Vitor Campagnolo Guizilini, Dennis Park, Wadim Kehl, Fredo Durand, Joshua B Tenenbaum, Vincent Sitzmann, Jiajun Wu, and Adrien Gaidon · 2021
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Zhipeng Bao, Pavel Tokmakov, Allan Jabri, Yu-Xiong Wang, Adrien Gaidon, and Martial Hebert · 2022
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Efficient geometry-aware 3d generative adversarial networks
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A simple baseline for bev perception without lidar
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3d scene painting via semantic image synthesis
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