| Autor: |
Wu, Yushuang, Yan, Zizheng, Chen, Ce, Wei, Lai, Li, Xiao, Li, Guanbin, Li, Yihao, Cui, Shuguang, Han, Xiaoguang |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
CVPR 2023 |
| Druh dokumentu: |
Working Paper |
| Popis: |
3D shape completion from point clouds is a challenging task, especially from scans of real-world objects. Considering the paucity of 3D shape ground truths for real scans, existing works mainly focus on benchmarking this task on synthetic data, e.g. 3D computer-aided design models. However, the domain gap between synthetic and real data limits the generalizability of these methods. Thus, we propose a new task, SCoDA, for the domain adaptation of real scan shape completion from synthetic data. A new dataset, ScanSalon, is contributed with a bunch of elaborate 3D models created by skillful artists according to scans. To address this new task, we propose a novel cross-domain feature fusion method for knowledge transfer and a novel volume-consistent self-training framework for robust learning from real data. Extensive experiments prove our method is effective to bring an improvement of 6%~7% mIoU. |
| Databáze: |
arXiv |
| Externí odkaz: |
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