Labeled Facets: New Surface Texture Dataset
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Object detection, recognition, segmentation, and retrieval have been at the forefront of 2D and 3D computer vision for a long time and have been utilized to address various problems in interdisciplinary domains. The 3D domain has not received as much attention as the 2D domain in several of these fields, and texture analysis in 3D is one of the least investigated. In the literature, there are several classic methods for retrieving and classifying 3D textures; however, research on facet-wise texture classification and segmentation is sparse. Moreover, in recent years deep learning excels in computer vision; utilizing its capacity for 3D texture analysis could improve performance compared to classical approaches. However, the scarcity of 3D texture data makes it challenging to employ deep learning. This paper presents a labeled 3D dataset based on already existing 3D datasets that can be utilized for texture classification, segmentation, and detection. The textures in the dataset are varied, with a wide range of surface variations. The dataset provides 3D texture surfaces annotated at the facet level, as well as fundamental geometric attributes such as curvature and shape index that can be utilized directly for further analysis. Download link for the dataset https://bit.ly/3wgSQgW.
Description
CCS Concepts: Computing methodologies → Mesh geometry models; Mesh models
@inproceedings{10.2312:3dor.20221181,
booktitle = {Eurographics Workshop on 3D Object Retrieval},
editor = {Berretti, Stefano and Thehoaris, Theoharis and Daoudi, Mohamed and Ferrari, Claudio and Veltkamp, Remco C.},
title = {{Labeled Facets: New Surface Texture Dataset}},
author = {Ganapathi, Iyyakutti Iyappan and Werghi, Naoufel},
year = {2022},
publisher = {The Eurographics Association},
ISSN = {1997-0471},
ISBN = {978-3-03868-174-8},
DOI = {10.2312/3dor.20221181}
}