| DualPoseNet: Category-level 6D Object Pose and Size Estimation Using Dual Pose Network with Refined Learning of Pose Consistency J Lin, Z Wei, Z Li, S Xu, K Jia, Y Li Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 124 | 2021 |
| Geometry-aware generation of adversarial point clouds Y Wen, J Lin, K Chen, CLP Chen, K Jia IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 | 100 | 2020 |
| Category-level 6d object pose and size estimation using self-supervised deep prior deformation networks J Lin, Z Wei, C Ding, K Jia European Conference on Computer Vision, 19-34, 2022 | 50 | 2022 |
| Deep multi-view learning using neuron-wise correlation-maximizing regularizers K Jia, J Lin, M Tan, D Tao IEEE Transactions on Image Processing 28 (10), 5121-5134, 2019 | 42 | 2019 |
| Sparse steerable convolutions: An efficient learning of se (3)-equivariant features for estimation and tracking of object poses in 3d space J Lin, H Li, K Chen, J Lu, K Jia Advances in Neural Information Processing Systems 34, 16779-16790, 2021 | 24 | 2021 |
| Sam-6d: Segment anything model meets zero-shot 6d object pose estimation J Lin, L Liu, D Lu, K Jia Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 15 | 2024 |
| Masked surfel prediction for self-supervised point cloud learning Y Zhang, J Lin, C He, Y Chen, K Jia, L Zhang arXiv preprint arXiv:2207.03111, 2022 | 15 | 2022 |
| Vi-net: Boosting category-level 6d object pose estimation via learning decoupled rotations on the spherical representations J Lin, Z Wei, Y Zhang, K Jia Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 14 | 2023 |
| Dcl-net: Deep correspondence learning network for 6d pose estimation H Li, J Lin, K Jia European Conference on Computer Vision, 369-385, 2022 | 12 | 2022 |
| Point-dae: Denoising autoencoders for self-supervised point cloud learning Y Zhang, J Lin, R Li, K Jia, L Zhang arXiv e-prints, arXiv: 2211.06841, 2022 | 7* | 2022 |
| Cad-pu: A curvature-adaptive deep learning solution for point set upsampling J Lin, X Shi, Y Gao, K Chen, K Jia arXiv preprint arXiv:2009.04660, 2020 | 5 | 2020 |
| Manifold-aware self-training for unsupervised domain adaptation on regressing 6D object pose Y Zhang, J Lin, K Chen, Z Xu, Y Wang, K Jia arXiv preprint arXiv:2305.10808, 2023 | 1 | 2023 |
| Method for point cloud up-sampling based on deep learning K Jia, J Lin, K Chen US Patent 11,880,959, 2024 | | 2024 |
Kui Jia (贾奎)The Chinese University of Hong Kong, ShenzhenVerified email at cuhk.edu.cn
