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Driven by the appealing properties of neural fields for storing and communicating 3D data, the problem of directly processing them to address tasks such as classification and part segmentation has emerged and has been investigated in recent works.
Marching cubes: A high resolution 3d surface construction algorithm
William E Lorensen and Harvey E Cline · 1987
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On the algebraic structure of feedforward network weight spaces
Robert Hecht-Nielsen · 1990
Earlier work this paper cites.
Voxnet: A 3d convolutional neural network for real-time object recognition
Daniel Maturana and Sebastian Scherer · 2015
Earlier work this paper cites.
3d shapenets: A deep representation for volumetric shapes
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
A scalable active framework for region annotation in 3d shape collections
Li Yi, Vladimir G. Kim, Duygu Ceylan, I-Chao Shen, Mengyan Yan, Hao Su, Cewu Lu, Qixing Huang, Alla Sheffer, and Leonidas Guibas · 2016
Earlier work this paper cites.
A point set generation network for 3d object reconstruction from a single image
Haoqiang Fan, Hao Su, and Leonidas J Guibas · 2017
Earlier work this paper cites.
Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Learning implicit fields for generative shape modeling
Zhiqin Chen and Hao Zhang · 2019
Earlier work this paper cites.
Occupancy networks: Learning 3d reconstruction in function space
Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger · 2019
Earlier work this paper cites.
Deepsdf: Learning continuous signed distance functions for shape representation
Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove · 2019
Earlier work this paper cites.
Pointdan: A multi-scale 3d domain adaption network for point cloud representation
Can Qin, Haoxuan You, Lichen Wang, C-C Jay Kuo, and Yun Fu · 2019
Earlier work this paper cites.
Scene representation networks: Continuous 3d-structure-aware neural scene representations
Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein · 2019
Earlier work this paper cites.
Super-convergence: Very fast training of neural networks using large learning rates
Leslie N Smith and Nicholay Topin · 2019
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What do single-view 3d reconstruction networks learn?
Maxim Tatarchenko, Stephan R Richter, René Ranftl, Zhuwen Li, Vladlen Koltun, and Thomas Brox · 2019
Cited alongside, same era.
Dynamic graph cnn for learning on point clouds
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon · 2019
Cited alongside, same era.
Disn: Deep implicit surface network for high-quality single-view 3d reconstruction
Qiangeng Xu, Weiyue Wang, Duygu Ceylan, Radomir Mech, and Ulrich Neumann · 2019
Cited alongside, same era.
Neural unsigned distance fields for implicit function learning
Julian Chibane, Gerard Pons-Moll, et al · 2020
Cited alongside, same era.
Implicit geometric regularization for learning shapes
Amos Gropp, Lior Yariv, Niv Haim, Matan Atzmon, and Yaron Lipman · 2020
Cited alongside, same era.
Local implicit grid representations for 3d scenes
Chiyu Jiang, Avneesh Sud, Ameesh Makadia, Jingwei Huang, Matthias Nießner, Thomas Funkhouser, et al · 2020
Neural fields in visual computing and beyond
Yiheng Xie, Towaki Takikawa, Shunsuke Saito, Or Litany, Shiqin Yan, Numair Khan, Federico Tombari, James Tompkin, Vincent Sitzmann, and Srinath Sridhar · 2021
Later among the works it cites.
Efficient geometry-aware 3d generative adversarial networks
Eric R. Chan, Connor Z. Lin, Matthew A. Chan, Koki Nagano, Boxiao Pan, Shalini de Mello, Orazio Gallo, Leonidas Guibas, Jonathan Tremblay, Sameh Khamis, Tero Karras, and Gordon Wetzstein · 2022
Later among the works it cites.
Tensorf: Tensorial radiance fields
Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su · 2022
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From data to functa: Your data point is a function and you can treat it like one
Emilien Dupont, Hyunjik Kim, SM Ali Eslami, Danilo Jimenez Rezende, and Dan Rosenbaum · 2022
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Plenoxels: Radiance fields without neural networks
Sara Fridovich-Keil, Alex Yu, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa · 2022
Later among the works it cites.
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Cited alongside, same era.
Meshwalker: Deep mesh understanding by random walks
Alon Lahav and Ayellet Tal · 2020
Cited alongside, same era.
Neural sparse voxel fields
Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt · 2020
Cited alongside, same era.
Convolutional occupancy networks
Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger · 2020
Cited alongside, same era.
Implicit neural representations with periodic activation functions
Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein · 2020
Cited alongside, same era.
Patchnets: Patch-based generalizable deep implicit 3d shape representations
Edgar Tretschk, Ayush Tewari, Vladislav Golyanik, Michael Zollhöfer, Carsten Stoll, and Christian Theobalt · 2020
Cited alongside, same era.
The role of permutation invariance in linear mode connectivity of neural networks
Rahim Entezari, Hanie Sedghi, Olga Saukh, and Behnam Neyshabur · 2021
Cited alongside, same era.
Subdivision-based mesh convolution networks
Shi-Min Hu, Zheng-Ning Liu, Meng-Hao Guo, Jun-Xiong Cai, Jiahui Huang, Tai-Jiang Mu, and Ralph R Martin · 2022
Later among the works it cites.
Learning deep implicit functions for 3d shapes with dynamic code clouds
Tianyang Li, Xin Wen, Yu-Shen Liu, Hua Su, and Zhizhong Han · 2022
Later among the works it cites.
Learning to generate 3d shapes from a single example
Rundi Wu and Changxi Zheng · 2022
Later among the works it cites.
Git re-basin: Merging models modulo permutation symmetries
Samuel Ainsworth, Jonathan Hayase, and Siddhartha Srinivasa · 2023
Closest in time.
Deep learning on implicit neural representations of shapes
Luca De Luigi, Adriano Cardace, Riccardo Spezialetti, Pierluigi Zama Ramirez, Samuele Salti, and Luigi Di Stefano · 2023
Closest in time.
Objaverse: A universe of annotated 3d objects
Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi · 2023
Closest in time.
Tri-miprf: Tri-mip representation for efficient anti-aliasing neural radiance fields
Wenbo Hu, Yuling Wang, Lin Ma, Bangbang Yang, Lin Gao, Xiao Liu, and Yuewen Ma · 2023
Closest in time.
Nerfacc: Efficient sampling accelerates nerfs
Ruilong Li, Hang Gao, Matthew Tancik, and Angjoo Kanazawa · 2023
Closest in time.
Equivariant architectures for learning in deep weight spaces
Aviv Navon, Aviv Shamsian, Idan Achituve, Ethan Fetaya, Gal Chechik, and Haggai Maron · 2023
Closest in time.
K-planes: Explicit radiance fields in space, time, and appearance
Sara Fridovich-Keil and Giacomo Meanti, Frederik Rahbæk Warburg, Benjamin Recht, and Angjoo Kanazawa · 2023
Closest in time.
Pet-neus: Positional encoding tri-planes for neural surfaces, 2023
Yiqun Wang, Ivan Skorokhodov, and Peter Wonka · 2023
Closest in time.