Deep learning for 3d point clouds: A survey Y Guo*, H Wang*, Q Hu*, H Liu*, L Liu, M Bennamoun IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), 2020 | 1806 | 2020 |
RandLA-Net: Efficient semantic segmentation of large-scale point clouds Q Hu, B Yang, L Xie, S Rosa, Y Guo, Z Wang, N Trigoni, A Markham IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2020), 2020 | 1617 | 2020 |
Learning object bounding boxes for 3d instance segmentation on point clouds B Yang, J Wang, R Clark, Q Hu, S Wang, A Markham, N Trigoni Advances in Neural Information Processing Systems (NeurIPS 2019), 6737-6746, 2019 | 330 | 2019 |
Axiom-based grad-cam: Towards accurate visualization and explanation of cnns R Fu, Q Hu, X Dong, Y Guo, Y Gao, B Li BMVC 2020, 2020 | 259 | 2020 |
SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration S Ao*, Q Hu*, B Yang, A Markham, Y Guo IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2021), 2020 | 249 | 2020 |
Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds Y Zhang, Q Hu*, G Xu, Y Ma, J Wan, Y Guo IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2022), 2022 | 239 | 2022 |
Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges Q Hu, B Yang, S Khalid, W Xiao, N Trigoni, A Markham IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2021), 2020 | 169 | 2020 |
Learning semantic segmentation of large-scale point clouds with random sampling Q Hu, B Yang, L Xie, S Rosa, Y Guo, Z Wang, N Trigoni, A Markham IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (11), 8338 …, 2021 | 137 | 2021 |
Sqn: Weakly-supervised semantic segmentation of large-scale 3d point clouds Q Hu, B Yang, G Fang, Y Guo, A Leonardis, N Trigoni, A Markham European Conference on Computer Vision (ECCV 2022), 2021 | 112 | 2021 |
Detecting and tracking small and dense moving objects in satellite videos: A benchmark Q Yin, Q Hu, H Liu, F Zhang, Y Wang, Z Lin, W An, Y Guo IEEE Transactions on Geoscience and Remote Sensing 60, 1-18, 2021 | 70 | 2021 |
Sensaturban: Learning semantics from urban-scale photogrammetric point clouds Q Hu, B Yang, S Khalid, W Xiao, N Trigoni, A Markham International Journal of Computer Vision 130 (2), 316-343, 2022 | 58 | 2022 |
STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset M Chen, Q Hu*, T Hugues, A Feng, Y Hou, K McCullough, L Soibelman arXiv preprint arXiv:2203.09065, 2022 | 54 | 2022 |
Roreg: Pairwise point cloud registration with oriented descriptors and local rotations H Wang, Y Liu, Q Hu, B Wang, J Chen, Z Dong, Y Guo, W Wang, B Yang IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 | 35 | 2023 |
Buffer: Balancing accuracy, efficiency, and generalizability in point cloud registration S Ao, Q Hu, H Wang, K Xu, Y Guo Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 25 | 2023 |
Robust long-term tracking via instance specific proposals H Liu, Q Hu, B Li, Y Guo IEEE Transactions on Instrumentation and Measurement (IEEE TIM), 2019 | 25 | 2019 |
Object tracking using multiple features and adaptive model updating Q Hu, Y Guo, Z Lin, W An, H Cheng IEEE Transactions on Instrumentation and Measurement 66 (11), 2882-2897, 2017 | 24 | 2017 |
Devnet: Self-supervised monocular depth learning via density volume construction K Zhou, L Hong, C Chen, H Xu, C Ye, Q Hu, Z Li ECCV 2022, 125-142, 2022 | 22 | 2022 |
3DAC: Learning Attribute Compression for Point Clouds G Fang, Q Hu, H Wang, Y Xu, Y Guo IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2022), 2022 | 21 | 2022 |
Continuous mapping convolution for large-scale point clouds semantic segmentation K Yan, Q Hu, H Wang, X Huang, L Li, S Ji IEEE Geoscience and Remote Sensing Letters 19, 1-5, 2021 | 20 | 2021 |
三维视觉前沿进展 龙霄潇, 程新景, 朱昊, 张朋举, 刘浩敏, 李俊, 郑林涛, 胡庆拥, 刘浩, ... 中国图象图形学报, 2021 | 20 | 2021 |