CC BY 4.0Neumann, KaiKaiNeumannSantos, PedroPedroSantosFellner, DieterDieterFellner2025-01-152025-01-152024https://doi.org/10.24406/publica-4080https://publica.fraunhofer.de/handle/publica/48134410.2312/gch.2024125710.24406/publica-4080Image-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.enBranche: Cultural and Creative EconomyResearch Line: Computer vision (CV)Research Line: Machine learning (ML)LTA: Machine intelligence, algorithms, and data structures (incl. semantics)LTA: Generation, capture, processing, and output of images and 3D modelsTexturing3D ReconstructionDeep learningconference paper