SCoDA: Domain Adaptive Shape Completion for Real Scans

• URL: http://arxiv.org/abs/2304.10179v2
• Date: Mon, 24 Apr 2023 06:31:59 GMT
• Title: SCoDA: Domain Adaptive Shape Completion for Real Scans
• Authors: Yushuang Wu, Zizheng Yan, Ce Chen, Lai Wei, Xiao Li, Guanbin Li, Yihao Li, Shuguang Cui, Xiaoguang Han
• Abstract summary: 3D shape completion from point clouds is a challenging task, especially from scans of real-world objects. We propose a new task, SCoDA, for the domain adaptation of real scan shape completion from synthetic data. 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.
• Score: 78.92028595499245
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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