Binary segmentation of relief patterns on point clouds

Analysis of 3D textures, also known as relief patterns is a challenging task that requires separating repetitive surface patterns from the underlying global geometry. Existing works classify entire surfaces based on one or a few patterns by extracting ad-hoc statistical properties. Unfortunately, th...

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Bibliographic Details
Published in:	Computers & graphics Vol. 123; p. 104020
Main Authors:	Paolini, Gabriele, Tortorici, Claudio, Berretti, Stefano
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.10.2024
Subjects:	3D segmentation Point cloud Relief pattern Relief pattern 3D segmentation Point cloud
ISSN:	0097-8493
Online Access:	Get full text
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Summary:	Analysis of 3D textures, also known as relief patterns is a challenging task that requires separating repetitive surface patterns from the underlying global geometry. Existing works classify entire surfaces based on one or a few patterns by extracting ad-hoc statistical properties. Unfortunately, these methods are not suitable for objects with multiple geometric textures and perform poorly on more complex shapes. In this paper, we propose a neural network for binary segmentation to infer per-point labels based on the presence of surface relief patterns. We evaluated the proposed architecture on a high resolution point cloud dataset, surpassing the state-of-the-art, while maintaining memory and computation efficiency. [Display omitted] •A deep learning model for geometric texture segmentation on 3D surfaces.•Architecture design based on distinctive features of relief patterns.•Application of geodesic Voronoi diagrams to reduce memory usage during training.
ISSN:	0097-8493
DOI:	10.1016/j.cag.2024.104020