One Million Scenes for Autonomous Driving: ONCE Dataset
暂无分享,去创建一个
Xiaodan Liang | Zhenguo Li | Wei Zhang | Chunjing Xu | Hang Xu | Jiageng Mao | Chenhan Jiang | Minzhe Niu | Chao Ye | Hanxue Liang | Yamin Li | Jie Yu
[1] M. Mahdi Roozbahani,et al. ScaleNet: An Unsupervised Representation Learning Method for Limited Information , 2023, GCPR.
[2] Xiaojuan Qi,et al. ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Rohit Girdhar,et al. Self-Supervised Pretraining of 3D Features on any Point-Cloud , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[4] Leonidas J. Guibas,et al. 3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[5] Leonidas J. Guibas,et al. PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding , 2020, ECCV.
[6] Philipp Krähenbühl,et al. Center-based 3D Object Detection and Tracking , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[7] Priya Goyal,et al. Unsupervised Learning of Visual Features by Contrasting Cluster Assignments , 2020, NeurIPS.
[8] Joachim Denzler,et al. Cityscapes 3D: Dataset and Benchmark for 9 DoF Vehicle Detection , 2020, ArXiv.
[9] Pierre H. Richemond,et al. Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning , 2020, NeurIPS.
[10] Yan Wang,et al. Train in Germany, Test in the USA: Making 3D Object Detectors Generalize , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[11] Jun Won Choi,et al. 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection , 2020, ECCV.
[12] Yohannes Kassahun,et al. A2D2: Audi Autonomous Driving Dataset , 2020, ArXiv.
[13] Quoc V. Le,et al. Meta Pseudo Labels , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Kaiming He,et al. Improved Baselines with Momentum Contrastive Learning , 2020, ArXiv.
[15] Geoffrey E. Hinton,et al. A Simple Framework for Contrastive Learning of Visual Representations , 2020, ICML.
[16] David Berthelot,et al. FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence , 2020, NeurIPS.
[17] Xiaogang Wang,et al. PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[18] Tat-Seng Chua,et al. SESS: Self-Ensembling Semi-Supervised 3D Object Detection , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[19] Dragomir Anguelov,et al. Scalability in Perception for Autonomous Driving: Waymo Open Dataset , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[20] Alex H. Lang,et al. PointPainting: Sequential Fusion for 3D Object Detection , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Nicholas Carlini,et al. ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring , 2019, ArXiv.
[22] Ross B. Girshick,et al. Momentum Contrast for Unsupervised Visual Representation Learning , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[23] Yuki M. Asano,et al. Self-labelling via simultaneous clustering and representation learning , 2019, ICLR.
[24] Quoc V. Le,et al. Self-Training With Noisy Student Improves ImageNet Classification , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[25] Armin Mustafa,et al. A*3D Dataset: Towards Autonomous Driving in Challenging Environments , 2019, 2020 IEEE International Conference on Robotics and Automation (ICRA).
[26] Simon Lucey,et al. Argoverse: 3D Tracking and Forecasting With Rich Maps , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[27] Bin Yang,et al. Multi-Task Multi-Sensor Fusion for 3D Object Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[28] David Berthelot,et al. MixMatch: A Holistic Approach to Semi-Supervised Learning , 2019, NeurIPS.
[29] Quoc V. Le,et al. Unsupervised Data Augmentation for Consistency Training , 2019, NeurIPS.
[30] Yin Zhou,et al. MVX-Net: Multimodal VoxelNet for 3D Object Detection , 2019, 2019 International Conference on Robotics and Automation (ICRA).
[31] Qiang Xu,et al. nuScenes: A Multimodal Dataset for Autonomous Driving , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Yi-Ting Chen,et al. The H3D Dataset for Full-Surround 3D Multi-Object Detection and Tracking in Crowded Urban Scenes , 2019, 2019 International Conference on Robotics and Automation (ICRA).
[33] Jiong Yang,et al. PointPillars: Fast Encoders for Object Detection From Point Clouds , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[34] Xiaogang Wang,et al. PointRCNN: 3D Object Proposal Generation and Detection From Point Cloud , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[35] Dinesh Manocha,et al. TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents , 2018, AAAI.
[36] Bo Li,et al. SECOND: Sparsely Embedded Convolutional Detection , 2018, Sensors.
[37] Bin Yang,et al. PIXOR: Real-time 3D Object Detection from Point Clouds , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[38] Trevor Darrell,et al. BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning , 2018, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Ruigang Yang,et al. The ApolloScape Dataset for Autonomous Driving , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[40] In So Kweon,et al. KAIST Multi-Spectral Day/Night Data Set for Autonomous and Assisted Driving , 2018, IEEE Transactions on Intelligent Transportation Systems.
[41] Steven Lake Waslander,et al. Joint 3D Proposal Generation and Object Detection from View Aggregation , 2017, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[42] W. Liu,et al. Frustum PointNets for 3D Object Detection from RGB-D Data , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[43] Yin Zhou,et al. VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[44] Shuigeng Zhou,et al. DeepCluster: A General Clustering Framework Based on Deep Learning , 2017, ECML/PKDD.
[45] Shin Ishii,et al. Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[46] Harri Valpola,et al. Weight-averaged consistency targets improve semi-supervised deep learning results , 2017, ArXiv.
[47] Ji Wan,et al. Multi-view 3D Object Detection Network for Autonomous Driving , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[48] Timo Aila,et al. Temporal Ensembling for Semi-Supervised Learning , 2016, ICLR.
[49] Paolo Favaro,et al. Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles , 2016, ECCV.
[50] Alexei A. Efros,et al. Colorful Image Colorization , 2016, ECCV.
[51] Michael I. Jordan,et al. Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.
[52] Victor S. Lempitsky,et al. Unsupervised Domain Adaptation by Backpropagation , 2014, ICML.
[53] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[54] Andreas Geiger,et al. Vision meets robotics: The KITTI dataset , 2013, Int. J. Robotics Res..
[55] Fei-Fei Li,et al. ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[56] Yuexin Ma,et al. TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents , 2018 .
[57] Dong-Hyun Lee,et al. Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks , 2013 .
[58] Christopher K. I. Williams,et al. The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.