Beyond Complete Shapes: A Benchmark for Quantitative Evaluation of 3D Shape Surface Matching Algorithms

TL;DR

This paper introduces BeCoS, a large, flexible benchmark dataset for evaluating 3D shape surface matching algorithms, addressing limitations of existing datasets by enabling realistic partial and full shape matching scenarios.

Contribution

The authors present a procedural framework for generating challenging shape matching datasets and create BeCoS, a comprehensive benchmark with over 2500 shapes for evaluating matching algorithms.

Findings

State-of-the-art methods evaluated on BeCoS

Benchmark covers both full and partial shape matching

Framework enables realistic and diverse dataset generation

Abstract

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.