PBR-SR: Mesh PBR Texture Super Resolution from 2D Image Priors
Yujin Chen, Yinyu Nie, Benjamin Ummenhofer, Reiner Birkl, Michael Paulitsch, Matthias Nie{\ss}ner

TL;DR
PBR-SR is a zero-shot method that enhances low-resolution PBR textures to high-resolution with high fidelity by leveraging pretrained image priors and multi-view constraints, without additional training.
Contribution
It introduces a novel zero-shot super-resolution technique for PBR textures that combines pretrained natural image priors with differentiable rendering and multi-view constraints.
Findings
Produces high-fidelity, high-resolution PBR textures from low-resolution inputs.
Outperforms existing methods in texture quality and rendering evaluations.
Operates without additional training or data, relying solely on pretrained priors.
Abstract
We present PBR-SR, a novel method for physically based rendering (PBR) texture super resolution (SR). It outputs high-resolution, high-quality PBR textures from low-resolution (LR) PBR input in a zero-shot manner. PBR-SR leverages an off-the-shelf super-resolution model trained on natural images, and iteratively minimizes the deviations between super-resolution priors and differentiable renderings. These enhancements are then back-projected into the PBR map space in a differentiable manner to produce refined, high-resolution textures. To mitigate view inconsistencies and lighting sensitivity, which is common in view-based super-resolution, our method applies 2D prior constraints across multi-view renderings, iteratively refining the shared, upscaled textures. In parallel, we incorporate identity constraints directly in the PBR texture domain to ensure the upscaled textures remain…
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Taxonomy
TopicsMedical Image Segmentation Techniques · Radiomics and Machine Learning in Medical Imaging · Medical Imaging Techniques and Applications
