DcMatch: Unsupervised Multi-Shape Matching with Dual-Level Consistency

• URL: http://arxiv.org/abs/2509.01204v1
• Date: Mon, 01 Sep 2025 07:43:11 GMT
• Title: DcMatch: Unsupervised Multi-Shape Matching with Dual-Level Consistency
• Authors: Tianwei Ye, Yong Ma, Xiaoguang Mei,
• Abstract summary: We introduce DcMatch, a novel unsupervised learning framework for non-rigid multi-shape matching.<n>We leverage a shape graph attention network to capture the underlying manifold structure of the entire shape collection.<n>Our method consistently outperforms previous state-of-the-art approaches across diverse multi-shape matching scenarios.
• Score: 10.661544717577389
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Establishing point-to-point correspondences across multiple 3D shapes is a fundamental problem in computer vision and graphics. In this paper, we introduce DcMatch, a novel unsupervised learning framework for non-rigid multi-shape matching. Unlike existing methods that learn a canonical embedding from a single shape, our approach leverages a shape graph attention network to capture the underlying manifold structure of the entire shape collection. This enables the construction of a more expressive and robust shared latent space, leading to more consistent shape-to-universe correspondences via a universe predictor. Simultaneously, we represent these correspondences in both the spatial and spectral domains and enforce their alignment in the shared universe space through a novel cycle consistency loss. This dual-level consistency fosters more accurate and coherent mappings. Extensive experiments on several challenging benchmarks demonstrate that our method consistently outperforms previous state-of-the-art approaches across diverse multi-shape matching scenarios. Code is available at https://github.com/YeTianwei/DcMatch.

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