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

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. Visualisations and code of our benchmark can be found at: https://nafieamrani.github.io/BeCoS/.
Description

        
@article{
10.1111:cgf.70186
, journal = {Computer Graphics Forum}, title = {{
Beyond Complete Shapes: A Benchmark for Quantitative Evaluation of 3D Shape Matching Algorithms
}}, author = {
Ehm, Viktoria
and
Amrani, Nafie El
and
Cremers, Daniel
and
Bernard, Florian
and
Xie, Yizheng
and
Bastian, Lennart
and
Gao, Maolin
and
Wang, Weikang
and
Sang, Lu
and
Cao, Dongliang
and
Weißberg, Tobias
and
Lähner, Zorah
}, year = {
2025
}, publisher = {
The Eurographics Association and John Wiley & Sons Ltd.
}, ISSN = {
1467-8659
}, DOI = {
10.1111/cgf.70186
} }
Citation
Collections