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Casting semantic segmentation of outdoor LiDAR point clouds as a 2D problem, e.g., via range projection, is an effective and popular approach.
ImageNet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Efficient inference in fully connected CRFs with gaussian edge potentials
Philipp Krähenbühl and Vladlen Koltun · 2011
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Are we ready for autonomous driving? The KITTI vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
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The pascal visual object classes challenge: A retrospective
Mark Everingham, SM Eslami, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2015
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The Cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
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CloudCompare
Daniel Girardeau-Montaut · 2016
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Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Cross modal distillation for supervision transfer
Saurabh Gupta, Judy Hoffman, and Jitendra Malik · 2016
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Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network
Wenzhe Shi, Jose Caballero, Ferenc Huszár, Johannes Totz, Andrew P. Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang · 2016
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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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SGDR: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
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PointNet: Deep learning on point sets for 3D classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 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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SEGCloud: Semantic segmentation of 3D point clouds
Lyne Tchapmi, Christopher Choy, Iro Armeni, JunYoung Gwak, and Silvio Savarese · 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
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The lovász-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks
Maxim Berman, Amal Rannen Triki, and Matthew B Blaschko · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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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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Large-scale point cloud semantic segmentation with superpoint graphs
Loic Landrieu and Martin Simonovsky · 2018
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 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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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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VV-Net: Voxel VAE net with group convolutions for point cloud segmentation
Hsien-Yu Meng, Lin Gao, Yu-Kun Lai, and Dinesh Manocha · 2019
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RangeNet++: Fast and accurate LiDAR semantic segmentation
Andres Milioto, Ignacio Vizzo, Jens Behley, and Cyrill Stachniss · 2019
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JSIS3D: Joint semantic-instance segmentation of 3D point clouds with multi-task pointwise networks and multi-value conditional random fields
Quang-Hieu Pham, Thanh Nguyen, Binh-Son Hua, Gemma Roig, and Sai-Kit Yeung · 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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Graph attention convolution for point cloud semantic segmentation
Lei Wang, Yuchun Huang, Yaolin Hou, Shenman Zhang, and Jie Shan · 2019
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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.
PointConv: Deep convolutional networks on 3D point clouds
Wenxuan Wu, Zhongang Qi, and Li Fuxin · 2019
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Lite-HDSeg: LiDAR semantic segmentation using lite harmonic dense convolutions
Ryan Razani, Ran Cheng, Ehsan Taghavi, and Liu Bingbing · 2021
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Segmenter: Transformer for semantic segmentation
Robin Strudel, Ricardo Garcia, Ivan Laptev, and Cordelia Schmid · 2021
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Going deeper with image transformers
Hugo Touvron, Matthieu Cord, Alexandre Sablayrolles, Gabriel Synnaeve, and Hervé Jégou · 2021
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PointAugmenting: Cross-modal augmentation for 3D object detection
Chunwei Wang, Chao Ma, Ming Zhu, and Xiaokang Yang · 2021
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Early convolutions help transformers see better
Tete Xiao, Mannat Singh, Eric Mintun, Trevor Darrell, Piotr Dollár, and Ross Girshick · 2021
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Image2Point: 3D point-cloud understanding with pretrained 2D convnets
Chenfeng Xu, Shijia Yang, Bohan Zhai, Bichen Wu, Xiangyu Yue, Wei Zhan, Peter Vajda, Kurt Keutzer, and Masayoshi Tomizuka · 2021
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Eren Erdal Aksoy, Saimir Baci, and Selcuk Cavdar · 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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End-to-end object detection with transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
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SalsaNext: Fast, uncertainty-aware semantic segmentation of LiDAR point clouds
Tiago Cortinhal, George Tzelepis, and Eren Erdal Aksoy · 2020
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3D-MPA: Multi-proposal aggregation for 3D semantic instance segmentation
Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, and Matthias Nießner · 2020
Cited alongside, same era.
OccuSeg: Occupancy-aware 3D instance segmentation
Lei Han, Tian Zheng, Lan Xu, and Lu Fang · 2020
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RandLA-Net: Efficient semantic segmentation of large-scale point clouds
Qingyong Hu, Bo Yang, Linhai Xie, Stefano Rosa, Yulan Guo, Zhihua Wang, Niki Trigoni, and Andrew Markham · 2020
Cited alongside, same era.
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Point Transformer
Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip HS Torr, and Vladlen Koltun · 2021
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Cylindrical and asymmetrical 3D convolution networks for LiDAR-based perception
Xinge Zhu, Hui Zhou, Tai Wang, Fangzhou Hong, Wei Li, Yuexin Ma, Hongsheng Li, Ruigang Yang, and Dahua Lin · 2021
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Perception-aware multi-sensor fusion for 3D LiDAR semantic segmentation
Zhuangwei Zhuang, Rong Li, Kui Jia, Qicheng Wang, Yuanqing Li, and Mingkui Tan · 2021
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TransFusion: Robust LiDAR-camera fusion for 3D object detection with transformers
Xuyang Bai, Zeyu Hu, Xinge Zhu, Qingqiu Huang, Yilun Chen, Hongbo Fu, and Chiew-Lan Tai · 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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PerceiverIO: A general architecture for structured inputs & outputs
Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, et al · 2022
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DeepFusion: LiDAR-camera deep fusion for multi-modal 3D object detection
Yingwei Li, Adams Wei Yu, Tianjian Meng, Ben Caine, Jiquan Ngiam, Daiyi Peng, Junyang Shen, Yifeng Lu, Denny Zhou, Quoc V Le, et al · 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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Pix4Point: Image pretrained transformers for 3D point cloud understanding
Guocheng Qian, Xingdi Zhang, Abdullah Hamdi, and Bernard Ghanem · 2022
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Image-to-LiDAR self-supervised distillation for autonomous driving data
Corentin Sautier, Gilles Puy, Spyros Gidaris, Alexandre Boulch, Andrei Bursuc, and Renaud Marlet · 2022
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Three things everyone should know about vision transformers
Hugo Touvron, Matthieu Cord, Alaaeldin El-Nouby, Jakob Verbeek, and Hervé Jégou · 2022
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Can we solve 3D vision tasks starting from a 2D vision transformer?
Yi Wang, Zhiwen Fan, Tianlong Chen, Hehe Fan, and Zhangyang Wang · 2022
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Bridged Transformer for vision and point cloud 3D object detection
Yikai Wang, TengQi Ye, Lele Cao, Wenbing Huang, Fuchun Sun, Fengxiang He, and Dacheng Tao · 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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CAT-Det: Contrastively augmented transformer for multi-modal 3D object detection
Yanan Zhang, Jiaxin Chen, and Di Huang · 2022
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Self-supervised point cloud representation learning with occlusion auto-encoder
Junsheng Zhou, Xin Wen, Yu-Shen Liu, Yi Fang, and Zhizhong Han · 2022
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FlexiViT: One model for all patch sizes
Lucas Beyer, Pavel Izmailov, Alexander Kolesnikov, Mathilde Caron, Simon Kornblith, Xiaohua Zhai, Matthias Minderer, Michael Tschannen, Ibrahim Alabdulmohsin, and Filip Pavetic · 2023
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