Unsupervised 3D Shape Parsing with Primitive Correspondence
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
3D shape parsing, the process of analyzing and breaking down a 3D shape into components or parts, has become an important task in computer graphics and vision. Approaches for shape parsing include segmentation and approximation methods. Approximation methods often represent shapes with a set of primitives fit to the shapes, such as cuboids, cylinders, or superquadrics. However, existing approximation methods typically rely on a large number of initial primitives and aim to maximize their coverage of the target shape, without accounting for correspondences among the primitives. In this paper, we introduce a novel 3D shape approximation method that integrates reconstruction and correspondence into a single objective, providing approximations that are consistent across the input set of shapes. Our method is unsupervised but also supports supervised learning. Experimental results demonstrate that integrating correspondences into the fitting process not only provides consistent correspondences across a set of input shapes, but also improves approximation quality when using a small number of primitives. Moreover, although correspondences are estimated in an unsupervised manner, our method effectively leverages this knowledge, leading to improved approximations.
Description
CCS Concepts: Computing methodologies → Shape modeling
@inproceedings{10.2312:pg.20251287,
booktitle = {Pacific Graphics Conference Papers, Posters, and Demos},
editor = {Christie, Marc and Han, Ping-Hsuan and Lin, Shih-Syun and Pietroni, Nico and Schneider, Teseo and Tsai, Hsin-Ruey and Wang, Yu-Shuen and Zhang, Eugene},
title = {{Unsupervised 3D Shape Parsing with Primitive Correspondence}},
author = {Zhao, Tianshu and Guan, Yanran and Kaick, Oliver van},
year = {2025},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-295-0},
DOI = {10.2312/pg.20251287}
}