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We propose a novel approach to self-supervised learning of point cloud representations by differentiable neural rendering.
Marching cubes: A high resolution 3d surface construction algorithm
William E Lorensen and Harvey E Cline · 1987
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Large scale multi-view stereopsis evaluation
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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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Faster r-cnn: Towards real-time object detection with region proposal networks
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Sun rgb-d: A rgb-d scene understanding benchmark suite
Shuran Song, Samuel P Lichtenberg, and Jianxiong Xiao · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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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Deformable convolutional networks
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas · 2017
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3d semantic segmentation with submanifold sparse convolutional networks
Benjamin Graham, Martin Engelcke, and Laurens van der Maaten · 2018
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4d spatio-temporal convnets: Minkowski convolutional neural networks
Christopher Choy, JunYoung Gwak, and Silvio Savarese · 2019
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Deep hough voting for 3d object detection in point clouds
Charles R Qi, Or Litany, Kaiming He, and Leonidas J Guibas · 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
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Kpconv: Flexible and deformable convolution for point clouds
Hugues Thomas, Charles R. Qi, Jean-Emmanuel Deschaud, Beatriz Marcotegui, François Goulette, and Leonidas J. Guibas · 2019
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Neural point-based graphics
Kara-Ali Aliev, Artem Sevastopolsky, Maria Kolos, Dmitry Ulyanov, and Victor Lempitsky · 2020
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nuscenes: A multimodal dataset for autonomous driving
Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Implicit functions in feature space for 3d shape reconstruction and completion
Julian Chibane, Thiemo Alldieck, and Gerard Pons-Moll · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Implicit geometric regularization for learning shapes
Amos Gropp, Lior Yariv, Niv Haim, Matan Atzmon, and Yaron Lipman · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Neural sparse voxel fields
Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt · 2020
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Convolutional occupancy networks
Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger · 2020
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Pointcontrast: Unsupervised pre-training for 3d point cloud understanding
Saining Xie, Jiatao Gu, Demi Guo, Charles R Qi, Leonidas Guibas, and Or Litany · 2020
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pixelNeRF: Neural radiance fields from one or few images
Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa · 2020
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NERF++: Analyzing and improving neural radiance fields
Kai Zhang, Gernot Riegler, Noah Snavely, and Vladlen Koltun · 2020
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Crosspoint: Self-supervised cross-modal contrastive learning for 3d point cloud understanding
Mohamed Afham, Isuru Dissanayake, Dinithi Dissanayake, Amaya Dharmasiri, Kanchana Thilakarathna, and Ranga Rodrigo · 2022
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4dcontrast: Contrastive learning with dynamic correspondences for 3d scene understanding
Yujin Chen, Matthias Nießner, and Angela Dai · 2022
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Masked autoencoders as spatiotemporal learners
Christoph Feichtenhofer, Haoqi Fan, Yanghao Li, and Kaiming He · 2022
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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A closer look at invariances in self-supervised pre-training for 3d vision
Lanxiao Li and Michael Heizmann · 2022
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H3dnet: 3d object detection using hybrid geometric primitives
Zaiwei Zhang, Bo Sun, Haitao Yang, and Qixing Huang · 2020
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Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields
Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan · 2021
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Multiresolution deep implicit functions for 3d shape representation
Zhang Chen, Yinda Zhang, Kyle Genova, Sean Fanello, Sofien Bouaziz, Christian Häne, Ruofei Du, Cem Keskin, Thomas Funkhouser, and Danhang Tang · 2021
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Exploring data-efficient 3d scene understanding with contrastive scene contexts
Ji Hou, Benjamin Graham, Matthias Nießner, and Saining Xie · 2021
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Pri3d: Can 3d priors help 2d representation learning?
Ji Hou, Saining Xie, Benjamin Graham, Angela Dai, and Matthias Nießner · 2021
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Spatio-temporal self-supervised representation learning for 3d point clouds
Siyuan Huang, Yichen Xie, Song-Chun Zhu, and Yixin Zhu · 2021
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Guided point contrastive learning for semi-supervised point cloud semantic segmentation
Li Jiang, Shaoshuai Shi, Zhuotao Tian, Xin Lai, Shu Liu, Chi-Wing Fu, and Jiaya Jia · 2021
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A closer look at invariances in self-supervised pre-training for 3d vision
Lanxiao Li and Michael Heizmann · 2022
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Unifying voxel-based representation with transformer for 3d object detection
Yanwei Li, Yilun Chen, Xiaojuan Qi, Zeming Li, Jian Sun, and Jiaya Jia · 2022
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Masked discrimination for self-supervised learning on point clouds
Haotian Liu, Mu Cai, and Yong Jae Lee · 2022
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Sparseneus: Fast generalizable neural surface reconstruction from sparse views
Xiaoxiao Long, Cheng Lin, Peng Wang, Taku Komura, and Wenping Wang · 2022
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Voxel-mae: Masked autoencoders for pre-training large-scale point clouds
Chen Min, Dawei Zhao, Liang Xiao, Yiming Nie, and Bin Dai · 2022
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Instant neural graphics primitives with a multiresolution hash encoding
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isdf: Real-time neural signed distance fields for robot perception
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Masked autoencoders for point cloud self-supervised learning
Yatian Pang, Wenxiao Wang, Francis EH Tay, Wei Liu, Yonghong Tian, and Li Yuan · 2022
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Npbg++: Accelerating neural point-based graphics
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Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training
Zhan Tong, Yibing Song, Jue Wang, and Limin Wang · 2022
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Go-surf: Neural feature grid optimization for fast, high-fidelity rgb-d surface reconstruction
Jingwen Wang, Tymoteusz Bleja, and Lourdes Agapito · 2022
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Point-nerf: Point-based neural radiance fields
Qiangeng Xu, Zexiang Xu, Julien Philip, Sai Bi, Zhixin Shu, Kalyan Sunkavalli, and Ulrich Neumann · 2022
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Point cloud pre-training with natural 3d structures
Ryosuke Yamada, Hirokatsu Kataoka, Naoya Chiba, Yukiyasu Domae, and Tetsuya Ogata · 2022
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Implicit autoencoder for point cloud self-supervised representation learning
Siming Yan, Zhenpei Yang, Haoxiang Li, Li Guan, Hao Kang, Gang Hua, and Qixing Huang · 2022
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Point-bert: Pre-training 3d point cloud transformers with masked point modeling
Xumin Yu, Lulu Tang, Yongming Rao, Tiejun Huang, Jie Zhou, and Jiwen Lu · 2022
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Point-m2ae: Multi-scale masked autoencoders for hierarchical point cloud pre-training
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Deep gradient learning for efficient camouflaged object detection
Ge-Peng Ji, Deng-Ping Fan, Yu-Cheng Chou, Dengxin Dai, Alexander Liniger, and Luc Van Gool · 2023
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Robust local light field synthesis via occlusion-aware sampling and deep visual feature fusion
Wenpeng Xing, Jie Chen, and Yike Guo · 2023
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