SHREC2026: Human perceived visual complexity of 3D shapes

Dyke, Roberto M., Deng, Yang, Brassey, Charlotte, Lai, Yukun ORCID: https://orcid.org/0000-0002-2094-5680 and Rosin, Paul L. ORCID: https://orcid.org/0000-0002-4965-3884 2026. SHREC2026: Human perceived visual complexity of 3D shapes. Computers & Graphics 139 , 104717. 10.1016/j.cag.2026.104717

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Abstract

The estimation of visual complexity in 3D remains a relatively under-studied problem. An aspect of this deficiency is due to the lack of suitable publicly available benchmarks. In this work, three datasets are used to investigate this problem from different angles: (1) a subset of the ABC dataset of CAD shapes, augmented with perceived visual complexity rankings; (2) an existing primate tooth dataset, with complexity rankings derived from established domain attributes; and (3) a novel dataset of fractal shapes with perceived visual complexity rankings. Ground truth rankings for (1) and (3) were derived from human judgments obtained from two studies in which lay individuals performed two-alternative forced-choice comparisons between pairs of shapes. We then invited researchers to submit results for algorithms designed to model shape complexity. We find that most methods that operate directly in 3D yield low, positive correlations; whereas—despite working on 2D projections derived from the original geometry—the best results were obtained by a subset of 2D methods.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Computational & Mathematical Sciences
Additional Information:	RRS applied. Full author list available at https://doi.org/10.1016/j.cag.2026.104717
Publisher:	Elsevier
ISSN:	0097-8493
Date of First Compliant Deposit:	3 September 2026
Date of Acceptance:	27 July 2026
Last Modified:	03 Sep 2026 12:00
URI:	https://orca.cardiff.ac.uk/id/eprint/188941

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