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.

该研究提出了一种新的任务 SCoDA，通过从合成数据到真实数据的领域自适应解决真实扫描形状完整性的问题，并使用一个新的数据集 ScanSalon 进行评估，提出了跨域特征融合方法和自我训练框架。实验证明改进了 6％至 7％的 mIoU。