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arXiv · 2509.01204

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

Tianwei Ye·Yong Ma·Xiaoguang Mei
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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.

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Tianwei Ye, Yong Ma, Xiaoguang Mei. 2025-09-01. DcMatch: Unsupervised Multi-Shape Matching with Dual-Level Consistency. https://arxiv.org/abs/2509.01204

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