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

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dc.contributor.authorEhm, Viktoriaen_US
dc.contributor.authorAmrani, Nafie Elen_US
dc.contributor.authorXie, Yizhengen_US
dc.contributor.authorBastian, Lennarten_US
dc.contributor.authorGao, Maolinen_US
dc.contributor.authorWang, Weikangen_US
dc.contributor.authorSang, Luen_US
dc.contributor.authorCao, Dongliangen_US
dc.contributor.authorWeißberg, Tobiasen_US
dc.contributor.authorLähner, Zorahen_US
dc.contributor.authorCremers, Danielen_US
dc.contributor.authorBernard, Florianen_US
dc.contributor.editorAttene, Marcoen_US
dc.contributor.editorSellán, Silviaen_US
dc.date.accessioned2025-06-20T07:39:16Z
dc.date.available2025-06-20T07:39:16Z
dc.date.issued2025
dc.description.abstractFinding 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/.en_US
dc.description.number5
dc.description.sectionheadersShape Analysis
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume44
dc.identifier.doi10.1111/cgf.70186
dc.identifier.issn1467-8659
dc.identifier.pages12 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.70186
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf70186
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleBeyond Complete Shapes: A Benchmark for Quantitative Evaluation of 3D Shape Matching Algorithmsen_US
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