UnReflectAnything: RGB-Only Highlight Removal by Rendering Synthetic Specular Supervision
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866918244178198528 |
|---|---|
| author | Rota, Alberto Kiray, Mert Karaoglu, Mert Asim Ruhkamp, Patrick De Momi, Elena Navab, Nassir Busam, Benjamin |
| author_facet | Rota, Alberto Kiray, Mert Karaoglu, Mert Asim Ruhkamp, Patrick De Momi, Elena Navab, Nassir Busam, Benjamin |
| contents | Specular highlights distort appearance, obscure texture, and hinder geometric reasoning in both natural and surgical imagery. We present UnReflectAnything, an RGB-only framework that removes highlights from a single image by predicting a highlight map together with a reflection-free diffuse reconstruction. The model uses a frozen vision transformer encoder to extract multi-scale features, a lightweight head to localize specular regions, and a token-level inpainting module that restores corrupted feature patches before producing the final diffuse image. To overcome the lack of paired supervision, we introduce a Virtual Highlight Synthesis pipeline that renders physically plausible specularities using monocular geometry, Fresnel-aware shading, and randomized lighting which enables training on arbitrary RGB images with correct geometric structure. UnReflectAnything generalizes across natural and surgical domains where non-Lambertian surfaces and non-uniform lighting create severe highlights and it achieves competitive performance with state-of-the-art results on several benchmarks. Project Page: https://alberto-rota.github.io/UnReflectAnything/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_09583 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UnReflectAnything: RGB-Only Highlight Removal by Rendering Synthetic Specular Supervision Rota, Alberto Kiray, Mert Karaoglu, Mert Asim Ruhkamp, Patrick De Momi, Elena Navab, Nassir Busam, Benjamin Computer Vision and Pattern Recognition Specular highlights distort appearance, obscure texture, and hinder geometric reasoning in both natural and surgical imagery. We present UnReflectAnything, an RGB-only framework that removes highlights from a single image by predicting a highlight map together with a reflection-free diffuse reconstruction. The model uses a frozen vision transformer encoder to extract multi-scale features, a lightweight head to localize specular regions, and a token-level inpainting module that restores corrupted feature patches before producing the final diffuse image. To overcome the lack of paired supervision, we introduce a Virtual Highlight Synthesis pipeline that renders physically plausible specularities using monocular geometry, Fresnel-aware shading, and randomized lighting which enables training on arbitrary RGB images with correct geometric structure. UnReflectAnything generalizes across natural and surgical domains where non-Lambertian surfaces and non-uniform lighting create severe highlights and it achieves competitive performance with state-of-the-art results on several benchmarks. Project Page: https://alberto-rota.github.io/UnReflectAnything/ |
| title | UnReflectAnything: RGB-Only Highlight Removal by Rendering Synthetic Specular Supervision |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.09583 |