PhysHDR: When Lighting Meets Materials and Scene Geometry in HDR Reconstruction

• URL: http://arxiv.org/abs/2509.16869v1
• Date: Sun, 21 Sep 2025 01:41:40 GMT
• Title: PhysHDR: When Lighting Meets Materials and Scene Geometry in HDR Reconstruction
• Authors: Hrishav Bakul Barua, Kalin Stefanov, Ganesh Krishnasamy, KokSheik Wong, Abhinav Dhall,
• Abstract summary: Low Dynamic Range to High Dynamic Range () image translation is a fundamental task in many computational vision problems.<n>Numerous data-driven methods have been proposed to address this problem; however, they lack explicit modeling of illumination, lighting, and scene geometry in images.<n>This paper presents Phys, a simple yet powerful latent diffusion-based generative model for HDR image reconstruction.
• Score: 13.694220697168829
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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