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We propose a new self-supervised method for pre-training the backbone of deep perception models operating on point clouds.
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3D ShapeNets: A deep representation for volumetric shapes
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Shuffle and learn: unsupervised learning using temporal order verification
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SGDR: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 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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Deep clustering for unsupervised learning of visual features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and M. Douze · 2018
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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AtlasNet: A papier-mâché approach to learning 3D surface generation
T. Groueix, M. Fisher, V.G. Kim, B.C. Russell, and M. Aubry · 2018
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3D-RCNN: Instance-level 3D Object Reconstruction via Render-and-Compare
Abhijit Kundu, Yin Li, and James M Rehg · 2018
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Deep marching cubes: Learning explicit surface representations
Y. Liao, S. Donne, and A. Geiger · 2018
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Learning efficient point cloud generation for dense 3D object reconstruction
C. Lin, C. Kong, and S. Lucey · 2018
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Pixel2Mesh: Generating 3D mesh models from single RGB images
N. Wang, Y. Zhang, Z. Li, Y. Fu, W. Liu, and Y.G. Jiang · 2018
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Unsupervised feature learning via non-parametric instance-level discrimination
Zhirong Wu, Yuanjun Xiong, Stella Yu, and Dahua Lin · 2018
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SECOND: Sparsely embedded convolutional detection
Yan Yan, Yuxing Mao, and Bo Li · 2018
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SemanticKITTI: A dataset for semantic scene understanding of lidar sequences
Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Cyrill Stachniss, and Jurgen Gall · 2019
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Learning implicit fields for generative shape modeling
Z. Chen and H. Zhang · 2019
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4D spatio-temporal ConvNets: Minkowski convolutional neural networks
Christopher Choy, JunYoung Gwak, and Silvio Savarese · 2019
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Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
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Mesh R-CNN
G. Gkioxari, J. Malik, and J. Johnson · 2019
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Unsupervised multi-task feature learning on point clouds
Kaveh Hassani and Mike Haley · 2019
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ABC: A big cad model dataset for geometric deep learning
Sebastian Koch, Albert Matveev, Zhongshi Jiang, Francis Williams, Alexey Artemov, Evgeny Burnaev, Marc Alexa, Denis Zorin, and Daniele Panozzo · 2019
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Pointpillars: Fast encoders for object detection from point clouds
Alex H. Lang, Sourabh Vora, Holger Caesar, Lubing Zhou, Jiong Yang, and Oscar Beijbom · 2019
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Point-voxel CNN for efficient 3D deep learning
Zhijian Liu, Haotian Tang, Yujun Lin, and Song Han · 2019
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Occupancy networks: Learning 3D reconstruction in function space
L. Mescheder, M. Oechsle, M. Niemeyer, S. Nowozin, and A. Geiger · 2019
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Implicit surface representations as layers in neural networks
M. Michalkiewicz, J.K. Pontes, D. Jack, M. Baktashmotlagh, and A. Eriksson · 2019
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DeepSDF: Learning continuous signed distance functions for shape representation
Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove · 2019
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PyTorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala · 2019
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Self-supervised deep learning on point clouds by reconstructing space
Jonathan Sauder and Bjarne Sievers · 2019
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Contrastive representation distillation
Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2020
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PointContrast: Unsupervised pre-training for 3D point cloud understanding
Saining Xie, Jiatao Gu, Demi Guo, Charles R. Qi, Leonidas J. Guibas, and Or Litany · 2020
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SALD: Sign agnostic learning with derivatives
Matan Atzmon and Yaron Lipman · 2021
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Beit: Bert pre-training of image transformers
Hangbo Bao, Li Dong, and Furu Wei · 2021
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NeeDrop: Self-supervised shape representation from sparse point clouds using needle dropping
Alexandre Boulch, Gilles Puy, and Renaud Marlet · 2021
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Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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PointFlow: 3D point cloud generation with continuous normalizing flows
G. Yang, X. Huang, Z. Hao, M. Liu, S.J. Belongie, and B. Hariharan · 2019
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Unsupervised feature learning for point cloud understanding by contrasting and clustering using graph convolutional neural networks
Ling Zhang and Zhigang Zhu · 2019
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SAL: Sign agnostic learning of shapes from raw data
Matan Atzmon and Yaron Lipman · 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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Learning gradient fields for shape generation
Ruojin Cai, Guandao Yang, Hadar Averbuch-Elor, Zekun Hao, Serge Belongie, Noah Snavely, and Bharath Hariharan · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, J. Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 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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Unsupervised learning of geometric sampling invariant representations for 3D point clouds
Haolan Chen, Shitong Luo, Xiang Gao, and Wei Hu · 2021
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Shape self-correction for unsupervised point cloud understanding
Ye Chen, Jinxian Liu, Bingbing Ni, Hang Wang, Jiancheng Yang, Ning Liu, Teng Li, and Qi Tian · 2021
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Self-contrastive learning with hard negative sampling for self-supervised point cloud learning
Bi’an Du, Xiang Gao, Wei Hu, and Xin Li · 2021
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OBoW: Online bag-of-visual-words generation for self-supervised learning
Spyros Gidaris, Andrei Bursuc, Gilles Puy, Nikos Komodakis, Matthieu Cord, and Patrick Pérez · 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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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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CoCoNets: Continuous contrastive 3D scene representations
Shamit Lal, Mihir Prabhudesai, Ishita Mediratta, Adam W. Harley, and Katerina Fragkiadaki · 2021
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Exploring geometry-aware contrast and clustering harmonization for self-supervised 3d object detection
Hanxue Liang, Chenhan Jiang, Dapeng Feng, Xin Chen, Hang Xu, Xiaodan Liang, Wei Zhang, Zhenguo Li, and Luc Van Gool · 2021
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Learning from 2D: Pixel-to-point knowledge transfer for 3D pretraining
Yueh-Cheng Liu, Yu-Kai Huang, HungYueh Chiang, Hung-Ting Su, Zhe Yu Liu, Chin-Tang Chen, Ching-Yu Tseng, and Winston H. Hsu · 2021
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DeepDT: Learning geometry from Delaunay triangulation for surface reconstruction
Yiming Luo, Zhenxing Mi, and Wenbing Tao · 2021
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One million scenes for autonomous driving: ONCE dataset
Jiageng Mao, Minzhe Niu, Chenhan Jiang, hanxue liang, Jingheng Chen, Xiaodan Liang, Yamin Li, Chaoqiang Ye, Wei Zhang, Zhenguo Li, Jie Yu, Hang Xu, and Chunjing Xu · 2021
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Unsupervised point cloud pre-training via occlusion completion
Hanchen Wang, Qi Liu, Xiangyu Yue, Joan Lasenby, and Matt J Kusner · 2021
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Unsupervised 3D learning for shape analysis via multiresolution instance discrimination
Peng-Shuai Wang, Yu-Qi Yang, Qian-Fang Zou, Zhirong Wu, Yang Liu, and Xin Tong · 2021
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Center-based 3D object detection and tracking
Tianwei Yin, Xingyi Zhou, and Philipp Krahenbuhl · 2021
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Self-supervised pretraining of 3D features on any point-cloud
Zaiwei Zhang, Rohit Girdhar, Armand Joulin, and Ishan Misra · 2021
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Cylindrical and asymmetrical 3D convolution networks for lidar segmentation
Xinge Zhu, Hui Zhou, Tai Wang, Fangzhou Hong, Yuexin Ma, Wei Li, Hongsheng Li, and Dahua Lin · 2021
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POCO: Point convolution for surface reconstruction
Alexandre Boulch and Renaud Marlet · 2022
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SimIPU: Simple 2D image and 3D point cloud unsupervised pre-training for spatial-aware visual representations
Zhenyu Li, Zehui Chen, Ang Li, Liangji Fang, Qinhong Jiang, Xianming Liu, Junjun Jiang, Bolei Zhou, and Hang Zhao · 2022
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Segcontrast: 3D point cloud feature representation learning through self-supervised segment discrimination
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Masked autoencoders for point cloud self-supervised learning
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Image-to-lidar self-supervised distillation for autonomous driving data
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Deep surface reconstruction from point clouds with visibility information
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Neural fields as learnable kernels for 3D reconstruction
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ProposalContrast: Unsupervised pre-training for lidar-based 3D object detection
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