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3D shape completion from point clouds is a challenging task, especially fromscans of real-world objects. Considering the paucity of 3D shape ground truthsfor real scans, existing works mainly focus on benchmarking this task onsynthetic data, e.g. 3D computer-aided design models. However, the domain gapbetween synthetic and real data limits the generalizability of these methods.Thus, we propose a new task, SCoDA, for the domain adaptation of real scanshape completion from synthetic data. A new dataset, ScanSalon, is contributedwith a bunch of elaborate 3D models created by skillful artists according toscans. To address this new task, we propose a novel cross-domain feature fusionmethod for knowledge transfer and a novel volume-consistent self-trainingframework for robust learning from real data. Extensive experiments prove ourmethod is effective to bring an improvement of 6%~7% mIoU.

SCoDA: Domain Adaptive Shape Completion for Real Scans