Unsupervised learning of general-purpose 3D shape feature and its application to analysis of minority 3D shapes

2023 Fiscal Year Final Research Report

• Project/Area Number: 21K17763
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 61010:Perceptual information processing-related
• Research Institution: University of Yamanashi
• Principal Investigator: FURUYA Takahiko 山梨大学, 大学院総合研究部, 准教授 (00770835)
• Project Period (FY): 2021-04-01 – 2024-03-31
• Keywords: 3次元形状 / 3次元点群 / 教師なし学習 / 自己教師あり学習 / 深層学習

Outline of Final Research Achievements

In this research, we focused on establishing techniques for unsupervised learning of general-purpose 3D shape features that can be applied to various types of 3D shape data. Specifically, we proposed (1) an unsupervised feature learning algorithm applicable to 3D shape data with diverse shape representations, (2) a novel DNN structure and its loss function for 3D point cloud shape completion, and (3) a new DNN structure and its unsupervised learning method for acquiring rotation-invariant 3D shape features. Through quantitative evaluations, we confirmed that each of the proposed methods demonstrates higher analysis accuracy compared to conventional methods. These research results are expected to improve the analysis accuracy of 3D shapes with a limited number of samples (minority 3D shapes), which have previously relied on handcrafted 3D shape features.

Free Research Field

3次元形状解析

Academic Significance and Societal Importance of the Research Achievements

本研究の学術的意義は，3D形状データの種類を問わず使える汎用型形状特徴量を教師なし深層学習で獲得する手法を提案したことである．これにより，ラベル付きデータが少ない3D形状の解析においても，ある程度高精度な特徴量を利用できる．
社会的意義として，製造業，建築，医療，娯楽作品制作といった3D形状データを活用する様々な分野において，3D形状解析の自動化・高精度化が期待できる．特に，ラベル付きデータの収集が困難な分野では本研究で提案した教師なし学習手法が有効に活用できると考えられる．さらには，各分野における3D形状データの活用が促進され，生産性の向上や新たな価値創造につながることが期待できる．