| The apolloscape dataset for autonomous driving X Huang, X Cheng, Q Geng, B Cao, D Zhou, P Wang, Y Lin, R Yang CVPRW 2018, 954-960, 2018 | 432 | 2018 |
| The apolloscape open dataset for autonomous driving and its application X Huang, P Wang, X Cheng, D Zhou, Q Geng, R Yang T-PAMI 2020, 2018 | 413 | 2018 |
| Iou loss for 2d/3d object detection D Zhou, J Fang, X Song, C Guan, J Yin, Y Dai, R Yang 3DV 2019, 85-94, 2019 | 266 | 2019 |
| Apollocar3d: A large 3d car instance understanding benchmark for autonomous driving X Song, P Wang, D Zhou, R Zhu, C Guan, Y Dai, H Su, H Li, R Yang CVPR 2019, 5452-5462, 2019 | 129 | 2019 |
| Lidar-based online 3d video object detection with graph-based message passing and spatiotemporal transformer attention J Yin, J Shen, C Guan, D Zhou, R Yang CVPR 2020, 11495-11504, 2020 | 114 | 2020 |
| Augmented lidar simulator for autonomous driving J Fang, D Zhou, F Yan, T Zhao, F Zhang, Y Ma, L Wang, R Yang RAL 2020 5 (2), 1931-1938, 2020 | 87 | 2020 |
| Joint 3d instance segmentation and object detection for autonomous driving D Zhou, J Fang, X Song, L Liu, J Yin, Y Dai, H Li, R Yang CVPR 2020, 1839-1849, 2020 | 79 | 2020 |
| Autoshape: Real-time shape-aware monocular 3d object detection Z Liu, D Zhou, F Lu, J Fang, L Zhang ICCV 2021, 15641-15650, 2021 | 72 | 2021 |
| Channel attention based iterative residual learning for depth map super-resolution X Song, Y Dai, D Zhou, L Liu, W Li, H Li, R Yang CVPR 2020, 5631-5640, 2020 | 67 | 2020 |
| FusionPainting: Multimodal fusion with adaptive attention for 3d object detection S Xu, D Zhou, J Fang, J Yin, Z Bin, L Zhang ITSC 2021, 3047-3054, 2021 | 61 | 2021 |
| Reliable scale estimation and correction for monocular visual odometry D Zhou, Y Dai, H Li IV 2016, 490-495, 2016 | 56 | 2016 |
| Moving object detection and segmentation in urban environments from a moving platform D Zhou, V Frémont, B Quost, Y Dai, H Li IVC 2017 68, 76-87, 2017 | 43 | 2017 |
| Ground-plane-based absolute scale estimation for monocular visual odometry D Zhou, Y Dai, H Li IEEE T-ITS 2019 21 (2), 791-802, 2019 | 42 | 2019 |
| Proposalcontrast: Unsupervised pre-training for lidar-based 3D object detection J Yin, D Zhou, L Zhang, J Fang, CZ Xu, J Shen, W Wang ECCV, 17-33, 2022 | 30 | 2022 |
| Simulating LIDAR point cloud for autonomous driving using real-world scenes and traffic flows J Fang, F Yan, T Zhao, F Zhang, D Zhou, R Yang, Y Ma, L Wang arXiv preprint arXiv:1811.07112 1, 2018 | 29 | 2018 |
| Dvi: Depth guided video inpainting for autonomous driving M Liao, F Lu, D Zhou, S Zhang, W Li, R Yang ECCV 2020, 1-17, 2020 | 27 | 2020 |
| LiDAR-Aug: A General Rendering-based Augmentation Framework for 3D Object Detection J Fang, X Zuo, D Zhou, S Jin, S Wang, L Zhang CVPR 2021, 4710-4720, 2021 | 26 | 2021 |
| MLDA-Net: multi-level dual attention-based network for self-supervised monocular depth estimation X Song, W Li, D Zhou, Y Dai, J Fang, H Li, L Zhang IEEE T-IP 2021 30, 4691-4705, 2021 | 23 | 2021 |
| Semi-supervised 3D Object Detection with Proficient Teachers J Yin, J Fang, D Zhou, L Zhang, CZ Xu, J Shen, W Wang ECCV 22, 727-743, 2022 | 18 | 2022 |
| On modeling ego-motion uncertainty for moving object detection from a mobile platform D Zhou, V Frémont, B Quost, B Wang IV 2014, 1332-1338, 2014 | 18 | 2014 |
Ruigang YangBaidu Research/Professor of Computer Science, University of KentuckyVerified email at cs.uky.edu
