无监督实现多形状点对点匹配，提升一致性与精度。
DcMatch: Unsupervised Multi-Shape Matching with Dual-Level Consistency
- 构建形状图注意力网络，学习全局流形结构以生成共享潜在空间。
- 在空间与谱域双重约束下，通过循环一致性损失提升匹配准确率。
- 适用于非刚性多形状匹配，尤其适合缺乏标注数据的场景。
在计算机视觉与图形学中，跨多个3D形状建立点对点对应关系是一项基础任务。本文提出DcMatch，一种新型无监督学习框架，用于非刚性多形状匹配。不同于现有方法仅从单一形状学习规范嵌入，本方法利用形状图注意力网络捕捉整个形状集合的底层流形结构，从而构建更具表达力和鲁棒性的共享潜在空间，通过宇宙预测器实现更一致的形状到宇宙对应。同时，将对应关系在空间域与谱域进行表示，并在共享宇宙空间中通过新颖的循环一致性损失强制对齐，实现双层次一致性，促进更精确且连贯的映射。在多个具有挑战性的基准测试上，实验表明该方法在多样化的多形状匹配场景中持续优于以往最先进方法。
原文摘要 · Abstract (English)
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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