3D Shape Segmentation Using Soft Density Peak Clustering and Semi-Supervised Learning

doi:10.1016/j.cad.2021.103181

3D Shape Segmentation Using Soft Density Peak Clustering and Semi-Supervised Learning

2022-04-01

16

OA

AI

DOI：10.1016/j.cad.2021.103181

Original

Save

View PDF

Abstract

En 中文

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. (C)& nbsp;2021 Elsevier Ltd. All rights reserved.

Keywords:

3D shapes

Segmentation

Semi-supervised

Deep learning

3D Shape Segmentation Using Soft Density Peak Clustering and Semi-Supervised Learning

Abstract

AI Summary

Journal

Organization