SCoDA: Domain Adaptive Shape Completion for Real Scans
Yushuang Wu, Zizheng Yan, Ce Chen, Lai Wei, Xiao Li, Guanbin Li, Yihao Li, Shuguang Cui
Abstract
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.
BibTeX
@inproceedings{cvpr2023_scodadomainadapt,
title = {SCoDA: Domain Adaptive Shape Completion for Real Scans},
author = {Yushuang Wu and Zizheng Yan and Ce Chen and Lai Wei and Xiao Li and Guanbin Li and Yihao Li and Shuguang Cui and Xiaoguang Han},
booktitle = {CVPR 2023},
year = {2023}
}