仅用一张RGB图去除高光，无需成对训练数据。
UnReflectAnything: RGB-Only Highlight Removal by Rendering Synthetic Specular Supervision
- 用冻结的视觉变换器提取多尺度特征，定位高光区域。
- 通过合成高光生成真实感反射，实现无监督训练。
- 适用于自然与手术图像，对非朗伯表面效果好。
镜面高光会扭曲外观、遮蔽纹理并影响几何推理，尤其在自然和手术图像中尤为严重。本文提出 UnReflectAnything，一种仅需单张RGB图像即可去除高光的框架，通过预测高光图并重建无反射的漫反射图像。模型采用冻结的视觉变换器编码器提取多尺度特征，轻量级头部定位镜面区域，再通过令牌级修复模块恢复受损特征块，最终生成漫反射图像。为解决缺乏成对标注的问题，引入虚拟高光合成管道，基于单目几何、菲涅耳感知着色和随机光照渲染物理合理的镜面反射，可在任意RGB图像上训练且保持正确几何结构。该方法在自然与手术图像中均表现良好，能有效处理非朗伯表面和非均匀光照下的严重高光，在多个基准上达到领先性能。
原文摘要 · Abstract (English)
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/
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