融合光照、材质与几何信息，提升低动态范围图像转高动态范围质量。
PhysHDR: When Lighting Meets Materials and Scene Geometry in HDR Reconstruction
- 基于潜空间扩散模型，用光照和深度条件引导重建。
- 引入新材料属性损失，显著改善金属、玻璃等表面的光影表现。
- 适合需要真实感渲染的3D生成与图像修复任务。
低动态范围（LDR）到高动态范围（HDR）图像转换是计算视觉中的基础任务。尽管已有大量数据驱动方法，但普遍缺乏对图像中光照、照明及场景几何的显式建模，限制了重建的HDR图像质量。由于不同材质（如镜面玻璃、金属与漫反射木材、石头）对光照和阴影的响应不同，建模材料特异性属性（如镜面与漫反射反射率）有望提升重建质量。本文提出PhysHDR，一种基于潜空间扩散的生成模型，通过在去噪过程中结合光照与深度信息，并利用新颖的损失函数融入场景表面的材质特性，实现高质量的HDR重建。实验结果表明，PhysHDR在多个近期先进方法中表现更优。
原文摘要 · Abstract (English)
Low Dynamic Range (LDR) to High Dynamic Range (HDR) image translation is a fundamental task in many computational vision problems. Numerous data-driven methods have been proposed to address this problem; however, they lack explicit modeling of illumination, lighting, and scene geometry in images. This limits the quality of the reconstructed HDR images. Since lighting and shadows interact differently with different materials, (e.g., specular surfaces such as glass and metal, and lambertian or diffuse surfaces such as wood and stone), modeling material-specific properties (e.g., specular and diffuse reflectance) has the potential to improve the quality of HDR image reconstruction. This paper presents PhysHDR, a simple yet powerful latent diffusion-based generative model for HDR image reconstruction. The denoising process is conditioned on lighting and depth information and guided by a novel loss to incorporate material properties of surfaces in the scene. The experimental results establish the efficacy of PhysHDR in comparison to a number of recent state-of-the-art methods.
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