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3D point-clouds and 2D images are different visual representations of the physical world.
Integral probability metrics and their generating classes of functions advances in applied probability
Alfred Müller · 1997
Earlier work this paper cites.
The discrepancy method - randomness and complexity
Bernard Chazelle · 2000
Earlier work this paper cites.
Rademacher processes and bounding the risk of function learning, 2004
Vladimir Koltchinskii and Dmitry Panchenko · 2004
Earlier work this paper cites.
Analysis of representations for domain adaptation
Shai Ben-david, John Blitzer, Koby Crammer, and Fernando Pereira · 2006
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning and domain adaptation
Yishay Mansour · 2009
Earlier work this paper cites.
Domain adaptation: Learning bounds and algorithms
Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh · 2009
Earlier work this paper cites.
Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite
A. Geiger, P. Lenz, and R. Urtasun · 2012
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
Earlier work this paper cites.
A review of point cloud registration algorithms for mobile robotics
François Pomerleau, Francis Colas, and Roland Siegwart · 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.
Deeppano: Deep panoramic representation for 3-d shape recognition
Baoguang Shi, Song Bai, Zhichao Zhou, and Xiang Bai · 2015
Earlier work this paper cites.
Sparse convolutional neural networks
Baoyuan Liu, Min Wang, Hassan Foroosh, Marshall Tappen, and Marianna Pensky · 2015
Earlier work this paper cites.
Multi-view convolutional neural networks for 3d shape recognition
Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik Learned-Miller · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Show, attend and tell: Neural image caption generation with visual attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio · 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.
Pointnet: Deep learning on point sets for 3d classification and segmentation, 2016
Charles R. Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas · 2016
Earlier work this paper cites.
Generative adversarial text to image synthesis
Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, and Honglak Lee · 2016
Earlier work this paper cites.
Joint 2d-3d-semantic data for indoor scene understanding
Iro Armeni, Sasha Sax, Amir R Zamir, and Silvio Savarese · 2017
Earlier work this paper cites.
Quo vadis, action recognition? a new model and the kinetics dataset
Joao Carreira and Andrew Zisserman · 2017
Earlier work this paper cites.
Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Charles R Qi, Li Yi, Hao Su, and Leonidas J Guibas · 2017
Earlier work this paper cites.
Deep projective 3d semantic segmentation
Felix Järemo Lawin, Martin Danelljan, Patrik Tosteberg, Goutam Bhat, Fahad Shahbaz Khan, and Michael Felsberg · 2017
Earlier work this paper cites.
Unstructured point cloud semantic labeling using deep segmentation networks
Alexandre Boulch, Bertrand Le Saux, and Nicolas Audebert · 2017
Earlier work this paper cites.
O-cnn: Octree-based convolutional neural networks for 3d shape analysis
Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun, and Xin Tong · 2017
Earlier work this paper cites.
Escape from cells: Deep kd-networks for the recognition of 3d point cloud models
Roman Klokov and Victor Lempitsky · 2017
Earlier work this paper cites.
Scannet: Richly-annotated 3d reconstructions of indoor scenes
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner · 2017
Earlier work this paper cites.
Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros · 2017
Earlier work this paper cites.
A lidar point cloud generator: from a virtual world to autonomous driving
Xiangyu Yue, Bichen Wu, Sanjit A Seshia, Kurt Keutzer, and Alberto L Sangiovanni-Vincentelli · 2018
Earlier work this paper cites.
Second: Sparsely embedded convolutional detection
Yan Yan, Yuxing Mao, and Bo Li · 2018
Earlier work this paper cites.
Fusing bird’s eye view lidar point cloud and front view camera image for 3d object detection
Zining Wang, Wei Zhan, and Masayoshi Tomizuka · 2018
Earlier work this paper cites.
Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud
Bichen Wu, Alvin Wan, Xiangyu Yue, and Kurt Keutzer · 2018
Earlier work this paper cites.
Pixor: Real-time 3d object detection from point clouds
Bin Yang, Wenjie Luo, and Raquel Urtasun · 2018
Earlier work this paper cites.
Pointcnn: Convolution on χ \chi -transformed points
Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, and Baoquan Chen · 2018
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Pointwise convolutional neural networks
Binh-Son Hua, Minh-Khoi Tran, and Sai-Kit Yeung · 2018
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So-net: Self-organizing network for point cloud analysis
Jiaxin Li, Ben M Chen, and Gim Hee Lee · 2018
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Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 2018
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3dmv: Joint 3d-multi-view prediction for 3d semantic scene segmentation
Angela Dai and Matthias Nießner · 2018
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3-d convolutional encoder-decoder network for low-dose ct via transfer learning from a 2-d trained network
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 visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
Later among the works it cites.
Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2020
Later among the works it cites.
Pre-training without natural images
Hirokatsu Kataoka, Kazushige Okayasu, Asato Matsumoto, Eisuke Yamagata, Ryosuke Yamada, Nakamasa Inoue, Akio Nakamura, and Yutaka Satoh · 2020
Later among the works it cites.
Big self-supervised models are strong semi-supervised learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Hongming Shan, Yi Zhang, Qingsong Yang, Uwe Kruger, Mannudeep K Kalra, Ling Sun, Wenxiang Cong, and Ge Wang · 2018
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Foundations of Machine Learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2018
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Multimodal unsupervised image-to-image translation
Xun Huang, Ming-Yu Liu, Serge Belongie, and Jan Kautz · 2018
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SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, C. Stachniss, and J. Gall · 2019
Cited alongside, same era.
Pseudo-lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving
Yan Wang, Wei-Lun Chao, Divyansh Garg, Bharath Hariharan, Mark Campbell, and Kilian Weinberger · 2019
Cited alongside, same era.
Single image depth estimation trained via depth from defocus cues
Shir Gur and Lior Wolf · 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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Data-efficient image recognition with contrastive predictive coding
Olivier Henaff · 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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Unsupervised point cloud pre-training via view-point occlusion, completion
Hanchen Wang, Qi Liu, Xiangyu Yue, Joan Lasenby, and Matthew J Kusner · 2020
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Weakly supervised semantic point cloud segmentation: Towards 10x fewer labels
Xun Xu and Gim Hee Lee · 2020
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Exploring data-efficient 3d scene understanding with contrastive scene contexts
Ji Hou, Benjamin Graham, Matthias Nießner, and Saining Xie · 2020
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Prevalence of neural collapse during the terminal phase of deep learning training
Vardan Papyan, X. Y. Han, and David L. Donoho · 2020
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Chenfeng Xu, Bohan Zhai, Bichen Wu, Tian Li, Wei Zhan, Peter Vajda, Kurt Keutzer, and Masayoshi Tomizuka · 2021
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3d modeling online free|3d warehouse models
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Virtual normal: Enforcing geometric constraints for accurate and robust depth prediction
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A simple and efficient multi-task network for 3d object detection and road understanding
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Self-supervised pretraining of visual features in the wild
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Self-supervised pretraining of 3d features on any point-cloud
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3d-to-2d distillation for indoor scene parsing
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Learning from 2d: Pixel-to-point knowledge transfer for 3d pretraining
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Pretrained transformers as universal computation engines
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Learning transferable visual models from natural language supervision
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Regularization strategy for point cloud via rigidly mixed sample
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