Kuffner dos Anjos, R orcid.org/0000-0002-2616-7541, Roberts, RA, Allen, B et al. (2 more authors) (2023) Saliency detection for large-scale mesh decimation. Computers and Graphics, 111. pp. 63-76. ISSN 0097-8493
Abstract
Highly complex and dense models of 3D objects have recently become indispensable in digital industries. Mesh decimation then plays a crucial role in the production pipeline to efficiently get visually convincing yet compact expressions of complex meshes. However, the current pipeline typically does not allow artists control the decimation process, just a simplification rate. Thus a preferred approach in production settings splits the process into a first pass of saliency detection highlighting areas of greater detail, and allowing artists to iterate until satisfied before simplifying the model. We propose a novel, efficient multi-scale method to compute mesh saliency at coarse and finer scales, based on fast mesh entropy of local surface measurements. Unlike previous approaches, we ensure a robust and straightforward calculation of mesh saliency even for densely tessellated models with millions of polygons. Moreover, we introduce a new adaptive subsampling and interpolation algorithm for saliency estimation. Our implementation achieves speedups of up to three orders of magnitude over prior approaches. Experimental results showcase its resilience to problem scenarios that efficiently scales up to process multi-million vertex meshes. Our evaluation with artists in the entertainment industry also demonstrates its applicability to real use-case scenarios.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) |
Keywords: | Geometric processing; Mesh saliency; Mesh decimation |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 01 Feb 2023 11:39 |
Last Modified: | 01 Feb 2023 11:39 |
Published Version: | http://dx.doi.org/10.1016/j.cag.2023.01.012 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.cag.2023.01.012 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:195788 |