DeepTex: Deep Learning-Based Texturing of Image-Based 3D Reconstructions
No Thumbnail Available
Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Image-based 3D reconstruction is a commonly used technique for measuring the geometry and color of objects or scenes based on images. While the geometry reconstruction of state-of-the-art approaches is mostly robust against varying lighting conditions and outliers, these pose a significant challenge for calculating an accurate texture map. This work proposes a deep-learning based texturing approach called ''DeepTex'' that uses a custom learned blending method on top of a traditional mosaic-based texturing approach. The model was trained using a custom synthetic data generation workflow and showed a significantly increased accuracy when generating textures in the presence of outliers and non-uniform lighting.
Description
CCS Concepts: Computing methodologies → Reconstruction; Computer vision; Artificial intelligence
@inproceedings{10.2312:gch.20241257,
booktitle = {Eurographics Workshop on Graphics and Cultural Heritage},
editor = {Corsini, Massimiliano and Ferdani, Daniele and Kuijper, Arjan and Kutlu, Hasan},
title = {{DeepTex: Deep Learning-Based Texturing of Image-Based 3D Reconstructions}},
author = {Neumann, Kai Alexander and Santos, Pedro and Fellner, Dieter W.},
year = {2024},
publisher = {The Eurographics Association},
ISSN = {2312-6124},
ISBN = {978-3-03868-248-6},
DOI = {10.2312/gch.20241257}
}