3D Shape Segmentation Using Soft Density Peak Clustering and Semi-Supervised Learning. (April 2022)

• Record Type: Journal Article
• Authors: Shu, Zhenyu; Yang, Sipeng; Wu, Haoyu; Xin, Shiqing; Pang, Chaoyi; Kavan, Ladislav; Liu, Ligang
• Is Part Of: Computer aided design. Volume 145(2022)
• Publication Date: 2022-04
• Subjects: 3D shapes -- Segmentation -- Semi-supervised -- Deep learning; Computer-aided design -- Periodicals; Engineering design -- Data processing -- Periodicals; Computer graphics -- Periodicals; Conception technique -- Informatique -- Périodiques; Infographie -- Périodiques; Computer graphics; Engineering design -- Data processing; Periodicals; Electronic journals; 620.00420285
• DOI: 10.1016/j.cad.2021.103181
• Languages: English
• ISSNs: 0010-4485

Abstract

Abstract: Shape segmentation plays a vital role in shape analysis. Recent research uses fully-supervised learning methods to achieve state-of-the-art performance. However, acquiring fully-labeled training data is usually an extremely expensive process. In this paper, we present a novel semi-supervised algorithm for 3D shape segmentation. In our method, users are only required to locate several seed faces with a very simple interaction by using our soft density peak clustering method. Our method can automatically learn the required label information and produce satisfactory segmentation results with our novel optimization model. Various experimental results show that the presented method can achieve superior segmentation performance over previous unsupervised methods and comparable performance to the fully-supervised methods. Highlights: We propose a novel and effective semi-supervised method for 3D shape segmentation. We extend the DPC algorithm to produce probability distributions instead of labels. Comparative experiments show that our algorithm outperforms previous methods.