Binary segmentation of relief patterns on point clouds - Paolini, Gabriele; Tortorici, Claudio; Berretti, Stefano (Computers & graphics, 2024)
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.