| 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 ECCV 2022, 600-619, 2022 | 127 | 2022 |
| Sqn: Weakly-supervised semantic segmentation of large-scale 3d point clouds with 1000x fewer labels Q Hu, B Yang, G Fang, Y Guo, A Leonardis, N Trigoni, A Markham arXiv preprint arXiv:2104.04891 2 (10), 2021 | 46 | 2021 |
| 3DAC: Learning Attribute Compression for Point Clouds G Fang, Q Hu, H Wang, Y Xu, Y Guo CVPR 2022, 2022 | 37 | 2022 |
| Mini-Splatting: Representing Scenes with a Constrained Number of Gaussians G Fang, B Wang ECCV 2024, 2024 | 33 | 2024 |
| 3DPointCaps++: Learning 3D Representations with Capsule Networks Y Zhao, G Fang, Y Guo, L Guibas, F Tombari, T Birdal IJCV 130 (9), 2321-2336, 2022 | 5 | 2022 |
| ACRF: Compressing Explicit Neural Radiance Fields via Attribute Compression G Fang, Q Hu, L Wang, Y Guo ICLR 2024, 2024 | 3 | 2024 |
| 4dac: Learning attribute compression for dynamic point clouds G Fang, Q Hu, Y Xu, Y Guo arXiv preprint arXiv:2204.11723, 2022 | 2 | 2022 |
| Mini-Splatting2: Building 360 Scenes within Minutes via Aggressive Gaussian Densification G Fang, B Wang arXiv preprint arXiv:2411.12788, 2024 | | 2024 |
| Supplementary Material–3DAC: Learning Attribute Compression for Point Clouds G Fang, Q Hu, H Wang, Y Xu, Y Guo | | |
