| Ntire 2017 challenge on single image super-resolution: Methods and results R Timofte, E Agustsson, L Van Gool, MH Yang, L Zhang Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 1244 | 2017 |
| Iou loss for 2d/3d object detection D Zhou, J Fang, X Song, C Guan, J Yin, Y Dai, R Yang 2019 International Conference on 3D Vision (3DV), 85-94, 2019 | 245 | 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 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 120 | 2019 |
| Deep depth super-resolution: Learning depth super-resolution using deep convolutional neural network X Song, Y Dai, X Qin Computer Vision–ACCV 2016: 13th Asian Conference on Computer Vision, Taipei …, 2017 | 93 | 2017 |
| 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 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 74 | 2020 |
| 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 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 65 | 2020 |
| Fcfr-net: Feature fusion based coarse-to-fine residual learning for depth completion L Liu, X Song, X Lyu, J Diao, M Wang, Y Liu, L Zhang Proceedings of the AAAI Conference on Artificial Intelligence 35 (3), 2136-2144, 2021 | 54 | 2021 |
| An autonomous excavator system for material loading tasks L Zhang, J Zhao, P Long, L Wang, L Qian, F Lu, X Song, D Manocha Science Robotics 6 (55), eabc3164, 2021 | 49 | 2021 |
| Deeply supervised depth map super-resolution as novel view synthesis X Song, Y Dai, X Qin IEEE Transactions on circuits and systems for video technology 29 (8), 2323-2336, 2018 | 41 | 2018 |
| Estimation of kinect depth confidence through self-training X Song, F Zhong, Y Wang, X Qin The Visual Computer 30, 855-865, 2014 | 26 | 2014 |
| 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 Transactions on Image Processing 30, 4691-4705, 2021 | 22 | 2021 |
| Optimization-based framework for excavation trajectory generation Y Yang, P Long, X Song, J Pan, L Zhang IEEE Robotics and Automation Letters 6 (2), 1479-1486, 2021 | 20* | 2021 |
| Self-supervised monocular depth estimation for all day images using domain separation L Liu, X Song, M Wang, Y Liu, L Zhang Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 17 | 2021 |
| Learning 2d-3d correspondences to solve the blind perspective-n-point problem L Liu, D Campbell, H Li, D Zhou, X Song, R Yang arXiv preprint arXiv:2003.06752, 2020 | 14 | 2020 |
| Iafa: Instance-aware feature aggregation for 3d object detection from a single image D Zhou, X Song, Y Dai, J Yin, F Lu, M Liao, J Fang, L Zhang Proceedings of the Asian Conference on Computer Vision, 2020 | 14 | 2020 |
| Self-supervised rigid transformation equivariance for accurate 3D point cloud registration Z Zhang, J Sun, Y Dai, D Zhou, X Song, M He Pattern Recognition 130, 108784, 2022 | 12 | 2022 |
| Modeling deviations of rgb-d cameras for accurate depth map and color image registration X Song, J Zheng, F Zhong, X Qin Multimedia Tools and Applications 77, 14951-14977, 2018 | 12 | 2018 |
| Mapfusion: A general framework for 3d object detection with hdmaps J Fang, D Zhou, X Song, L Zhang 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2021 | 11 | 2021 |
| Rotpredictor: Unsupervised canonical viewpoint learning for point cloud classification J Fang, D Zhou, X Song, S Jin, R Yang, L Zhang 2020 International Conference on 3D Vision (3DV), 987-996, 2020 | 8 | 2020 |
| Edge-guided depth map enhancement X Song, H Huang, F Zhong, X Ma, X Qin 2016 23rd International Conference on Pattern Recognition (ICPR), 2758-2763, 2016 | 6 | 2016 |
Dingfu ZhouBaidu IncVerified email at baidu.com
