  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.
