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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| Vydané v: | Computers & graphics Ročník 123; s. 104020 |
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| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
01.10.2024
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| ISSN: | 0097-8493 |
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| Abstract | 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.
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•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. |
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| AbstractList | 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. |
| ArticleNumber | 104020 |
| Author | Tortorici, Claudio Berretti, Stefano Paolini, Gabriele |
| Author_xml | – sequence: 1 givenname: Gabriele orcidid: 0009-0003-5325-2076 surname: Paolini fullname: Paolini, Gabriele email: gabriele.paolini1@unifi.it organization: Media Integration and Communication Center, University of Florence, Florence, 50134, Italy – sequence: 2 givenname: Claudio surname: Tortorici fullname: Tortorici, Claudio email: claudio.tortorici@tii.ae organization: Technology Innovation Institute, Abu Dhabi, 9639, United Arab Emirates – sequence: 3 givenname: Stefano surname: Berretti fullname: Berretti, Stefano email: stefano.berretti@unifi.it organization: Media Integration and Communication Center, University of Florence, Florence, 50134, Italy |
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| Keywords | Relief pattern 3D segmentation Point cloud |
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