Thông tin tài liệu
Nhan đề : |
Semi-supervised 3D shape segmentation with multilevel consistency and part substitution |
Tác giả : |
Chun-Yu, Sun Yu-Qi, Yang Hao-Xiang, Guo |
Năm xuất bản : |
2023 |
Nhà xuất bản : |
Springer |
Tóm tắt : |
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. |
Mô tả: |
CC BY |
URI: |
https://link.springer.com/article/10.1007/s41095-022-0281-9 https://dlib.phenikaa-uni.edu.vn/handle/PNK/7325 |
Bộ sưu tập |
OER - Công nghệ thông tin |
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