A Deep Learning Driven Active Framework for Segmentation of Large 3D Shape Collections. (March 2022)

• Record Type: Journal Article
• Authors: George, David; Xie, Xianghua; Lai, Yukun; Tam, Gary K.L.
• Is Part Of: Computer aided design. Volume 144(2022)
• Publication Date: 2022-03
• Subjects: Shape segmentation -- Active learning -- Shape collections -- User interaction; 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.2021.103179
• Languages: English
• ISSNs: 0010-4485

Abstract

Abstract: High-level shape understanding and technique evaluation on large repositories of 3D shapes often benefit from additional information known about the shapes. One example of such information is the semantic segmentation of a shape into functional or meaningful parts. Generating accurate segmentations with meaningful segment boundaries is, however, a costly process, typically requiring large amounts of user time to achieve high-quality results. In this paper we propose an active learning framework for large dataset segmentation, which iteratively provides the user with new predictions by training new models based on already segmented shapes. Our proposed pipeline consists of three components. First, we propose a fast and accurate feature-based deep learning model to provide dataset-wide segmentation predictions. Second, we develop an information theory measure to estimate the prediction quality and for ordering subsequent fast and meaningful shape selection. Our experiments show that such suggestive ordering helps to reduce users' time and effort, produce high-quality predictions, and construct a model that generalizes well. Lastly, we provide interactive segmentation refinement tools, helping the user quickly correct any prediction errors. We show that our framework is more accurate and in general more efficient than the state-of-the-art for large dataset segmentation, while also providing consistent segment boundaries. Highlights: The first deep learning driven active framework for segmentation of 3D collection. There are three essential key components of our framework: - a deep learning model balancing speed and accuracy - an information-metric for effective ordering - an interactive boundary refinement taking curvature and thickness into account. Extensive experiments showing the usefulness of the framework. Source code of our tools will be released for the community. Some new annotations or remeshed shapes for existing datasets will also be released.