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Implicit Neural Representations (INRs) have emerged in the last few years as a powerful tool to encode continuously a variety of different signals like images, videos, audio and 3D shapes.
End-to-end learning local multi-view descriptors for 3d point clouds
Lei Li, Siyu Zhu, Hongbo Fu, Ping Tan, and Chiew-Lan Tai · 1928
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Marching cubes: A high resolution 3d surface construction algorithm
William E Lorensen and Harvey E Cline · 1987
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Efficient multipoint aerodynamic design optimization via cokriging
David JJ Toal and Andy J Keane · 2011
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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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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Geodesic convolutional neural networks on riemannian manifolds
Jonathan Masci, Davide Boscaini, Michael Bronstein, and Pierre Vandergheynst · 2015
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Voxnet: A 3d convolutional neural network for real-time object recognition
Daniel Maturana and Sebastian Scherer · 2015
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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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Learning shape correspondence with anisotropic convolutional neural networks
Davide Boscaini, Jonathan Masci, Emanuele Rodolà, and Michael Bronstein · 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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Unsupervised learning of 3d structure from images
Danilo Jimenez Rezende, SM Eslami, Shakir Mohamed, Peter Battaglia, Max Jaderberg, and Nicolas Heess · 2016
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Volumetric and multi-view cnns for object classification on 3d data
Charles R Qi, Hao Su, Matthias Nießner, Angela Dai, Mengyuan Yan, and Leonidas J Guibas · 2016
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Deep sliding shapes for amodal 3d object detection in rgb-d images
Shuran Song and Jianxiong Xiao · 2016
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Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling
Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, and Josh Tenenbaum · 2016
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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
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Scannet: Richly-annotated 3d reconstructions of indoor scenes
Angela Dai, Angel X Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner · 2017
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A point set generation network for 3d object reconstruction from a single image
Haoqiang Fan, Hao Su, and Leonidas J Guibas · 2017
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
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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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Geometric deep learning on graphs and manifolds using mixture model cnns
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, and Michael M Bronstein · 2017
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Learning representations and generative models for 3d point clouds
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas · 2018
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Point convolutional neural networks by extension operators
Matan Atzmon, Haggai Maron, and Yaron Lipman · 2018
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Geodesic convolutional shape optimization
Pierre Baque, Edoardo Remelli, Francois Fleuret, and Pascal Fua · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2018
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Pointwise convolutional neural networks
Binh-Son Hua, Minh-Khoi Tran, and Sai-Kit Yeung · 2018
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Learning 3d shape completion from laser scan data with weak supervision
David Stutz and Andreas Geiger · 2018
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Learning three-dimensional flow for interactive aerodynamic design
Nobuyuki Umetani and Bernd Bickel · 2018
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Spidercnn: Deep learning on point sets with parameterized convolutional filters
Yifan Xu, Tianqi Fan, Mingye Xu, Long Zeng, and Yu Qiao · 2018
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Pvnet: A joint convolutional network of point cloud and multi-view for 3d shape recognition
Haoxuan You, Yifan Feng, Rongrong Ji, and Yue Gao · 2018
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Learning implicit fields for generative shape modeling
Zhiqin Chen and Hao Zhang · 2019
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Meshnet: Mesh neural network for 3d shape representation
Yutong Feng, Yifan Feng, Haoxuan You, Xibin Zhao, and Yue Gao · 2019
Cited alongside, same era.
Learning shape templates with structured implicit functions
Kyle Genova, Forrester Cole, Daniel Vlasic, Aaron Sarna, William T. Freeman, and Thomas Funkhouser · 2019
Cited alongside, same era.
Surface networks via general covers
Niv Haim, Nimrod Segol, Heli Ben-Hamu, Haggai Maron, and Yaron Lipman · 2019
Cited alongside, same era.
Meshcnn: a network with an edge
Rana Hanocka, Amir Hertz, Noa Fish, Raja Giryes, Shachar Fleishman, and Daniel Cohen-Or · 2019
Cited alongside, same era.
Texturenet: Consistent local parametrizations for learning from high-resolution signals on meshes
Jingwei Huang, Haotian Zhang, Li Yi, Thomas Funkhouser, Matthias Nießner, and Leonidas J Guibas · 2019
Cited alongside, same era.
Deepgcns: Can gcns go as deep as cnns?
Guohao Li, Matthias Muller, Ali Thabet, and Bernard Ghanem · 2019
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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Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision
Michael Niemeyer, Lars Mescheder, Michael Oechsle, and Andreas Geiger · 2020
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Convolutional occupancy networks
Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger · 2020
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Accelerating 3d deep learning with pytorch3d
Nikhila Ravi, Jeremy Reizenstein, David Novotny, Taylor Gordon, Wan-Yen Lo, Justin Johnson, and Georgia Gkioxari · 2020
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Dualconvmesh-net: Joint geodesic and euclidean convolutions on 3d meshes
Jonas Schult, Francis Engelmann, Theodora Kontogianni, and Bastian Leibe · 2020
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Cited alongside, same era.
Meshsnet: Deep multi-scale mesh feature learning for end-to-end tooth labeling on 3d dental surfaces
Chunfeng Lian, Li Wang, Tai-Hsien Wu, Mingxia Liu, Francisca Durán, Ching-Chang Ko, and Dinggang Shen · 2019
Cited alongside, same era.
Occupancy networks: Learning 3d reconstruction in function space
Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger · 2019
Cited alongside, same era.
Implicit surface representations as layers in neural networks
Mateusz Michalkiewicz, Jhony K Pontes, Dominic Jack, Mahsa Baktashmotlagh, and Anders Eriksson · 2019
Cited alongside, same era.
Occupancy flow: 4d reconstruction by learning particle dynamics
Michael Niemeyer, Lars Mescheder, Michael Oechsle, and Andreas Geiger · 2019
Cited alongside, same era.
Texture fields: Learning texture representations in function space
Michael Oechsle, Lars Mescheder, Michael Niemeyer, Thilo Strauss, and Andreas Geiger · 2019
Cited alongside, same era.
Deepsdf: Learning continuous signed distance functions for shape representation
Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove · 2019
Cited alongside, same era.
Pv-rcnn: Point-voxel feature set abstraction for 3d object detection
Shaoshuai Shi, Chaoxu Guo, Li Jiang, Zhe Wang, Jianping Shi, Xiaogang Wang, and Hongsheng Li · 2020
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Fourier features let networks learn high frequency functions in low dimensional domains
Matthew Tancik, Pratul Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan Barron, and Ren Ng · 2020
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Predicting neural network accuracy from weights
Thomas Unterthiner, Daniel Keysers, Sylvain Gelly, Olivier Bousquet, and Ilya O. Tolstikhin · 2020
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Pfcnn: Convolutional neural networks on 3d surfaces using parallel frames
Yuqi Yang, Shilin Liu, Hao Pan, Yang Liu, and Xin Tong · 2020
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Learning signal-agnostic manifolds of neural fields
Yilun Du, Katie Collins, Josh Tenenbaum, and Vincent Sitzmann · 2021
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The role of permutation invariance in linear mode connectivity of neural networks
Rahim Entezari, Hanie Sedghi, Olga Saukh, and Behnam Neyshabur · 2021
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Scf-net: Learning spatial contextual features for large-scale point cloud segmentation
Siqi Fan, Qiulei Dong, Fenghua Zhu, Yisheng Lv, Peijun Ye, and Fei-Yue Wang · 2021
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Pct: Point cloud transformer
Meng-Hao Guo, Jun-Xiong Cai, Zheng-Ning Liu, Tai-Jiang Mu, Ralph R Martin, and Shi-Min Hu · 2021
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Generating adversarial examples with graph neural networks
Florian Jaeckle and M Pawan Kumar · 2021
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Parameter prediction for unseen deep architectures
Boris Knyazev, Michal Drozdzal, Graham W. Taylor, and Adriana Romero · 2021
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Sp-gan: Sphere-guided 3d shape generation and manipulation
Ruihui Li, Xianzhi Li, Ka-Hei Hui, and Chi-Wing Fu · 2021
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Acorn: Adaptive coordinate networks for neural scene representation
Julien N. P. Martel, David B. Lindell, Connor Z. Lin, Eric R. Chan, Marco Monteiro, and Gordon Wetzstein · 2021
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Variational relational point completion network
Liang Pan, Xinyi Chen, Zhongang Cai, Junzhe Zhang, Haiyu Zhao, Shuai Yi, and Ziwei Liu · 2021
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Self-supervised representation learning on neural network weights for model characteristic prediction
Konstantin Schürholt, Dimche Kostadinov, and Damian Borth · 2021
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Hodgenet: learning spectral geometry on triangle meshes
Dmitriy Smirnov and Justin Solomon · 2021
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Implicit neural representations for image compression
Yannick Strümpler, Janis Postels, Ren Yang, Luc Van Gool, and Federico Tombari · 2021
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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, and Sanja Fidler · 2021
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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
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Paconv: Position adaptive convolution with dynamic kernel assembling on point clouds
Mutian Xu, Runyu Ding, Hengshuang Zhao, and Xiaojuan Qi · 2021
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Implicit neural video compression
Yunfan Zhang, Ties van Rozendaal, Johann Brehmer, Markus Nagel, and Taco Cohen · 2021
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Point transformer
Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip HS Torr, and Vladlen Koltun · 2021
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Git re-basin: Merging models modulo permutation symmetries
Samuel K Ainsworth, Jonathan Hayase, and Siddhartha Srinivasa · 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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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
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Learning smooth neural functions via lipschitz regularization
Hsueh-Ti Derek Liu, Francis Williams, Alec Jacobson, Sanja Fidler, and Or Litany · 2022
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Instant neural graphics primitives with a multiresolution hash encoding
Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller · 2022
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A structured dictionary perspective on implicit neural representations
Gizem Yüce, Guillermo Ortiz-Jiménez, Beril Besbinar, and Pascal Frossard · 2022
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