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Flowchart of the proposed method (HSLC)

Flowchart of the proposed method (HSLC)

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Unsupervised domain adaptation (UDA) is a technique for learning from a label-rich source domain and transferring the learned knowledge to an unlabeled target domain. Current researches on feature-based UDA methods usually utilize the pseudo labels to find new feature representations that can minimize the distribution difference between the two dom...

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Point cloud completion is a fundamental yet not well-solved problem in 3D vision. Current approaches often rely on 3D coordinate information and/or additional data (e.g., images and scanning viewpoints) to fill in missing parts. Unlike these methods, we explore self-structure augmentation and propose PointSea for global-to-local point cloud complet...