构建首个大规模3D形状匹配基准，支持完整与部分形变的定量评估。
Beyond Complete Shapes: A Benchmark for Quantitative Evaluation of 3D Shape Surface Matching Algorithms
- 基于程序化生成框架，可灵活构造完整与部分形状匹配数据集。
- 创建包含2543个形状的BeCoS基准，覆盖多种真实场景下的部分匹配问题。
- 适用于深度学习方法的训练与评估，尤其适合关注形状匹配的研究者。
寻找可变形3D形状之间的对应关系是几何处理、计算机视觉、图形学等领域长期存在的关键问题。现有形状匹配数据集多为静态或规模有限，难以适应包括完整与部分形状匹配在内的多种任务场景。特别是现有部分形状匹配数据集规模小（少于100个形状），不适合数据密集型机器学习方法；且其部分性常为人工构造，缺乏现实性。为此，我们提出一种通用且灵活的程序化生成框架，可跨形状传播自定义标注，适用于多种应用。利用该框架并手动建立七个现有完整几何形状匹配数据集间的跨数据集对应关系，我们构建了新的大型基准BeCoS，共包含2543个形状。基于此，我们设计了涵盖完整与部分匹配的多个挑战性评估场景，并对当前主流方法进行了基线测试。
原文摘要 · Abstract (English)
Finding correspondences between 3D deformable shapes is an important and long-standing problem in geometry processing, computer vision, graphics, and beyond. While various shape matching datasets exist, they are mostly static or limited in size, restricting their adaptation to different problem settings, including both full and partial shape matching. In particular the existing partial shape matching datasets are small (fewer than 100 shapes) and thus unsuitable for data-hungry machine learning approaches. Moreover, the type of partiality present in existing datasets is often artificial and far from realistic. To address these limitations, we introduce a generic and flexible framework for the procedural generation of challenging full and partial shape matching datasets. Our framework allows the propagation of custom annotations across shapes, making it useful for various applications. By utilising our framework and manually creating cross-dataset correspondences between seven existing (complete geometry) shape matching datasets, we propose a new large benchmark BeCoS with a total of 2543 shapes. Based on this, we offer several challenging benchmark settings, covering both full and partial matching, for which we evaluate respective state-of-the-art methods as baselines.
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