RelEdgePool: Relational Edge-Aware Pooling for n-gonal manifold and non-manifold 3D meshes

RelEdgePool: Relational Edge-Aware Pooling for n-gonal manifold and non-manifold 3D meshes | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 22 September 2025 V1 Latest version Share on RelEdgePool: Relational Edge-Aware Pooling for n-gonal manifold and non-manifold 3D meshes Author : Haroon Rashid 0009-0002-1150-3829 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175856984.48726132/v1 359 views 126 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract 3D computer vision has become an important topic in research and engineering applications. Various methods and algorithms have been introduced to make it possible to train neural networks on 3D mesh data. However, the complexity and size of mesh data make it difficult to perform batch processing and hierarchical feature extraction on it. An efficient pooling operation is needed to reduce meshes of higher vertex density to lower vertex density, while maintaining geometric resemblance between the input and pooled meshes. In this paper, I propose a novel algorithm for pooling mesh datasets. My algorithm utilizes edge-based vertex averaging for pooling and a novel approach, "Vertex Relational Clustering," to re-establish connectivity in pooled meshes. The proposed algorithm is deterministic in nature, improving training stability and reducing variability in gradient flow, contributing to more efficient optimization. The edge-based vertex averaging component is fully differentiable, allowing gradient flow during training, while the vertex relational clustering component for edge re-establishment is treated as a non-differentiable structural operation that does not require backpropagation. The algorithm supports n-gonal manifold and non-manifold meshes directly, without any need for triangulation or remeshing, making it broadly applicable and versatile. Supplementary Material File (reledgepool.pdf) Download 6.36 MB Information & Authors Information Version history V1 Version 1 22 September 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords 3d computer vision algorithm artificial intelligence computer vision deep learning Authors Affiliations Haroon Rashid 0009-0002-1150-3829 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 359 views 126 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Haroon Rashid. RelEdgePool: Relational Edge-Aware Pooling for n-gonal manifold and non-manifold 3D meshes. Authorea . 22 September 2025. DOI: https://doi.org/10.22541/au.175856984.48726132/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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