Neural-pull: Learning signed distance functions from point clouds by learning to pull space onto surfaces B Ma, Z Han, YS Liu, M Zwicker arXiv preprint arXiv:2011.13495, 2020 | 109 | 2020 |
Surface reconstruction from point clouds by learning predictive context priors B Ma, YS Liu, M Zwicker, Z Han Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 49 | 2022 |
Reconstructing 3D shapes from multiple sketches using direct shape optimization Z Han, B Ma, YS Liu, M Zwicker IEEE Transactions on Image Processing 29, 8721-8734, 2020 | 48 | 2020 |
Learning consistency-aware unsigned distance functions progressively from raw point clouds J Zhou, B Ma, YS Liu, Y Fang, Z Han Advances in neural information processing systems 35, 16481-16494, 2022 | 47 | 2022 |
Reconstructing surfaces for sparse point clouds with on-surface priors B Ma, YS Liu, Z Han Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 46 | 2022 |
Neaf: Learning neural angle fields for point normal estimation S Li, J Zhou, B Ma, YS Liu, Z Han Proceedings of the AAAI conference on artificial intelligence 37 (1), 1396-1404, 2023 | 22 | 2023 |
Towards better gradient consistency for neural signed distance functions via level set alignment B Ma, J Zhou, YS Liu, Z Han Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 17 | 2023 |
Uni3d: Exploring unified 3d representation at scale J Zhou, J Wang, B Ma, YS Liu, T Huang, X Wang arXiv preprint arXiv:2310.06773, 2023 | 12 | 2023 |
Learning a more continuous zero level set in unsigned distance fields through level set projection J Zhou, B Ma, S Li, YS Liu, Z Han Proceedings of the IEEE/CVF international conference on computer vision …, 2023 | 12 | 2023 |
Learning signed distance functions from noisy 3d point clouds via noise to noise mapping B Ma, YS Liu, Z Han | 10 | 2023 |
Self-supervised point cloud representation learning with occlusion auto-encoder J Zhou, X Wen, B Ma, YS Liu, Y Fang, Z Han arXiv e-prints, arXiv: 2203.14084, 2022 | 10 | 2022 |
Geodream: Disentangling 2d and geometric priors for high-fidelity and consistent 3d generation B Ma, H Deng, J Zhou, YS Liu, T Huang, X Wang arXiv preprint arXiv:2311.17971, 2023 | 6 | 2023 |
3D-OAE: Occlusion auto-encoders for self-supervised learning on point clouds J Zhou, X Wen, B Ma, YS Liu, Y Gao, Y Fang, Z Han arXiv preprint arXiv:2203.14084, 2022 | 6 | 2022 |
Differentiable registration of images and lidar point clouds with voxelpoint-to-pixel matching J Zhou, B Ma, W Zhang, Y Fang, YS Liu, Z Han Advances in Neural Information Processing Systems 36, 2024 | 4 | 2024 |
Learning continuous implicit field with local distance indicator for arbitrary-scale point cloud upsampling S Li, J Zhou, B Ma, YS Liu, Z Han Proceedings of the AAAI Conference on Artificial Intelligence 38 (4), 3181-3189, 2024 | 2 | 2024 |
CAP-UDF: Learning Unsigned Distance Functions Progressively from Raw Point Clouds with Consistency-Aware Field Optimization J Zhou, B Ma, S Li, YS Liu, Y Fang, Z Han IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024 | | 2024 |
Udiff: Generating conditional unsigned distance fields with optimal wavelet diffusion J Zhou, W Zhang, B Ma, K Shi, YS Liu, Z Han arXiv preprint arXiv:2404.06851, 2024 | | 2024 |
Supplementary Material for “Differentiable Registration of Images and LiDAR Point Clouds with VoxelPoint-to-Pixel Matching” J Zhou, B Ma, W Zhang, Y Fang, YS Liu, Z Han | | |
Supplemental Materials for “Learning a More Continuous Zero Level Set in Unsigned Distance Fields through Level Set Projection” J Zhou, B Ma, S Li, YS Liu, Z Han | | |
Supplemental Material “Surface Reconstruction from Point Clouds by Learning Predictive Context Priors” B Ma, YS Liu, M Zwicker, Z Han | | |