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• W4386076637 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." @default.
• W4386076637 created "2023-08-23" @default.
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• W4386076637 date "2023-06-01" @default.
• W4386076637 modified "2023-10-01" @default.
• W4386076637 title "SCoDA: Domain Adaptive Shape Completion for Real Scans" @default.
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• W4386076637 doi "https://doi.org/10.1109/cvpr52729.2023.01691" @default.
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