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Despite the tremendous progress of Masked Autoencoders (MAE) in developing vision tasks such as image and video, exploring MAE in large-scale 3D point clouds remains challenging due to the inherent irregularity.
Are we ready for autonomous driving? the KITTI vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
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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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Deepcluster: A general clustering framework based on deep learning
Kai Tian, Shuigeng Zhou, and Jihong Guan · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Second: Sparsely embedded convolutional detection
Yan Yan, Yuxing Mao, and Bo Li · 2018
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Pixor: Real-time 3d object detection from point clouds
Bin Yang, Wenjie Luo, and Raquel Urtasun · 2018
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Voxelnet: End-to-end learning for point cloud based 3d object detection
Yin Zhou and Oncel Tuzel · 2018
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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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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 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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Pointrcnn: 3d object proposal generation and detection from point cloud
Shaoshuai Shi, Xiaogang Wang, and Hongsheng Li · 2019
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Std: Sparse-to-dense 3d object detector for point cloud
Zetong Yang, Yanan Sun, Shu Liu, Xiaoyong Shen, and Jiaya Jia · 2019
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End-to-end multi-view fusion for 3d object detection in lidar point clouds
Yin Zhou, Pei Sun, Yu Zhang, Dragomir Anguelov, Jiyang Gao, Tom Ouyang, James Guo, Jiquan Ngiam, and Vijay Vasudevan · 2019
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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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Bootstrap your own latent - A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Ávila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal Valko · 2020
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Structure aware single-stage 3d object detection from point cloud
Chenhang He, Hui Zeng, Jianqiang Huang, Xian-Sheng Hua, and Lei Zhang · 2020
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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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From points to parts: 3d object detection from point cloud with part-aware and part-aggregation network
Shaoshuai Shi, Zhe Wang, Jianping Shi, Xiaogang Wang, and Hongsheng Li · 2020
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Point-gnn: Graph neural network for 3d object detection in a point cloud
Weijing Shi and Raj Rajkumar · 2020
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Scalability in perception for autonomous driving: Waymo open dataset
Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, Vijay Vasudevan, Wei Han, Jiquan Ngiam, Hang Zhao, Aleksei Timofeev, Scott Ettinger, Maxim Krivokon, Amy Gao, Aditya Joshi, Yu Zhang, Jonathon Shlens, Zhifeng Chen, and Dragomir Anguelov · 2020
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Pillar-based object detection for autonomous driving
Yue Wang, Alireza Fathi, Abhijit Kundu, David A. Ross, Caroline Pantofaru, Thomas A. Funkhouser, and Justin Solomon · 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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3dssd: Point-based 3d single stage object detector
Zetong Yang, Yanan Sun, Shu Liu, and Jiaya Jia · 2020
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Hvnet: Hybrid voxel network for lidar based 3d object detection
Maosheng Ye, Shuangjie Xu, and Tongyi Cao · 2020
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Voxel r-cnn: Towards high performance voxel-based 3d object detection
Jiajun Deng, Shaoshuai Shi, Peiwei Li, Wengang Zhou, Yanyong Zhang, and Houqiang Li · 2021
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Rangedet: In defense of range view for lidar-based 3d object detection
Lue Fan, Xuan Xiong, Feng Wang, Naiyan Wang, and Zhaoxiang Zhang · 2021
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Lidar r-cnn: An efficient and universal 3d object detector
Zhichao Li, Feng Wang, and Naiyan Wang · 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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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
Cited alongside, same era.
Suppress-and-refine framework for end-to-end 3d object detection
Zili Liu, Guodong Xu, Honghui Yang, Minghao Chen, Kuoliang Wu, Zheng Yang, Haifeng Liu, and Deng Cai · 2021
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Group-free 3d object detection via transformers
Ze Liu, Zheng Zhang, Yue Cao, Han Hu, and Xin Tong · 2021
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Pyramid r-cnn: Towards better performance and adaptability for 3d object detection
Jiageng Mao, Minzhe Niu, Haoyue Bai, Xiaodan Liang, Hang Xu, and Chunjing Xu · 2021
Cited alongside, same era.
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, Chunjing Xu, and Hang Xu · 2021
Cited alongside, same era.
Embracing single stride 3d object detector with sparse transformer
Lue Fan, Ziqi Pang, Tianyuan Zhang, Yu-Xiong Wang, Hang Zhao, Feng Wang, Naiyan Wang, and Zhaoxiang Zhang · 2022
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Fully sparse 3d object detection
Lue Fan, Feng Wang, Naiyan Wang, and Zhaoxiang Zhang · 2022
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Convmae: Masked convolution meets masked autoencoders
Peng Gao, Teli Ma, Hongsheng Li, Ziyi Lin, Jifeng Dai, and Yu Qiao · 2022
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M3DETR: multi-representation, multi-scale, mutual-relation 3d object detection with transformers
Tianrui Guan, Jun Wang, Shiyi Lan, Rohan Chandra, Zuxuan Wu, Larry Davis, and Dinesh Manocha · 2022
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Voxel set transformer: A set-to-set approach to 3d object detection from point clouds
Chenhang He, Ruihuang Li, Shuai Li, and Lei Zhang · 2022
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Voxel transformer for 3d object detection
Jiageng Mao, Yujing Xue, Minzhe Niu, Haoyue Bai, Jiashi Feng, Xiaodan Liang, Hang Xu, and Chunjing Xu · 2021
Cited alongside, same era.
Pvgnet: A bottom-up one-stage 3d object detector with integrated multi-level features
Zhenwei Miao, Jikai Chen, Hongyu Pan, Ruiwen Zhang, Kaixuan Liu, Peihan Hao, Jun Zhu, Yang Wang, and Xin Zhan · 2021
Cited alongside, same era.
An end-to-end transformer model for 3d object detection
Ishan Misra, Rohit Girdhar, and Armand Joulin · 2021
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3d object detection with pointformer
Xuran Pan, Zhuofan Xia, Shiji Song, Li Erran Li, and Gao Huang · 2021
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Improving 3d object detection with channel-wise transformer
Hualian Sheng, Sijia Cai, Yuan Liu, Bing Deng, Jianqiang Huang, Xian-Sheng Hua, and Min-Jian Zhao · 2021
Cited alongside, same era.
Shaoshuai Shi, Li Jiang, Jiajun Deng, Zhe Wang, Chaoxu Guo, Jianping Shi, Xiaogang Wang, and Hongsheng Li · 2021
Cited alongside, same era.
RSN: range sparse net for efficient, accurate lidar 3d object detection
Pei Sun, Weiyue Wang, Yuning Chai, Gamaleldin Elsayed, Alex Bewley, Xiao Zhang, Cristian Sminchisescu, and Dragomir Anguelov · 2021
Cited alongside, same era.
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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Masked autoencoders for self-supervised learning on automotive point clouds
Georg Hess, Johan Jaxing, Elias Svensson, David Hagerman, Christoffer Petersson, and Lennart Svensson · 2022
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Afdetv2: Rethinking the necessity of the second stage for object detection from point clouds
Yihan Hu, Zhuangzhuang Ding, Runzhou Ge, Wenxin Shao, Li Huang, Kun Li, and Qiang Liu · 2022
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Stratified transformer for 3d point cloud segmentation
Xin Lai, Jianhui Liu, Li Jiang, Liwei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, and Jiaya Jia · 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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Masked discrimination for self-supervised learning on point clouds
Haotian Liu, Mu Cai, and Yong Jae Lee · 2022
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3d-queryis: A query-based framework for 3d instance segmentation
Jiaheng Liu, Tong He, Honghui Yang, Rui Su, Jiayi Tian, Junran Wu, Hongcheng Guo, Ke Xu, and Wanli Ouyang · 2022
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Masked autoencoders for point cloud self-supervised learning
Yatian Pang, Wenxiao Wang, Francis E. H. Tay, Wei Liu, Yonghong Tian, and Li Yuan · 2022
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Fast point transformer
Chunghyun Park, Yoonwoo Jeong, Minsu Cho, and Jaesik Park · 2022
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Pillarnet: Real-time and high-performance pillar-based 3d object detection
Guangsheng Shi, Ruifeng Li, and Chao Ma · 2022
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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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Rbgnet: Ray-based grouping for 3d object detection
Haiyang Wang, Shaoshuai Shi, Ze Yang, Rongyao Fang, Qi Qian, Hongsheng Li, Bernt Schiele, and Liwei Wang · 2022
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Point transformer V2: grouped vector attention and partition-based pooling
Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, and Hengshuang Zhao · 2022
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Sparse fuse dense: Towards high quality 3d detection with depth completion
Xiaopei Wu, Liang Peng, Honghui Yang, Liang Xie, Chenxi Huang, Chengqi Deng, Haifeng Liu, and Deng Cai · 2022
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Point2seq: Detecting 3d objects as sequences
Yujing Xue, Jiageng Mao, Minzhe Niu, Hang Xu, Michael Bi Mi, Wei Zhang, Xiaogang Wang, and Xinchao Wang · 2022
Closest in time.
Graph R-CNN: towards accurate 3d object detection with semantic-decorated local graph
Honghui Yang, Zili Liu, Xiaopei Wu, Wenxiao Wang, Wei Qian, Xiaofei He, and Deng Cai · 2022
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A unified query-based paradigm for point cloud understanding
Zetong Yang, Li Jiang, Yanan Sun, Bernt Schiele, and Jiaya Jia · 2022
Closest in time.
Proposalcontrast: Unsupervised pre-training for lidar-based 3d object detection
Junbo Yin, Dingfu Zhou, Liangjun Zhang, Jin Fang, Cheng-Zhong Xu, Jianbing Shen, and Wenguan Wang · 2022
Closest in time.
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
Closest in time.
Point-m2ae: Multi-scale masked autoencoders for hierarchical point cloud pre-training
Renrui Zhang, Ziyu Guo, Peng Gao, Rongyao Fang, Bin Zhao, Dong Wang, Yu Qiao, and Hongsheng Li · 2022
Closest in time.
Not all points are equal: Learning highly efficient point-based detectors for 3d lidar point clouds
Yifan Zhang, Qingyong Hu, Guoquan Xu, Yanxin Ma, Jianwei Wan, and Yulan Guo · 2022
Closest in time.
Centerformer: Center-based transformer for 3d object detection
Zixiang Zhou, Xiangchen Zhao, Yu Wang, Panqu Wang, and Hassan Foroosh · 2022
Closest in time.