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    • Semi-supervised 3D shape segmentation with multilevel consistency and part substitution
    Authors: Chun-Yu, Sun; Yu-Qi, Yang; Hao-Xiang, Guo;  Advisor: -;  Co-Author: - (2023)

    The lack of fine-grained 3D shape segmentation data is the main obstacle to developing learning-based 3D segmentation techniques. We propose an effective semi-supervised method for learning 3D segmentations from a few labeled 3D shapes and a large amount of unlabeled 3D data. For the unlabeled data, we present a novel multilevel consistency loss to enforce consistency of network predictions between perturbed copies of a 3D shape at multiple levels: point level, part level, and hierarchical level. For the labeled data, we develop a simple yet effective part substitution scheme to augment the labeled 3D shapes with more structural variations to enhance training.