PhysHDR: When Lighting Meets Materials and Scene Geometry in HDR Reconstruction
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918144975568896 |
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| author | Barua, Hrishav Bakul Stefanov, Kalin Krishnasamy, Ganesh Wong, KokSheik Dhall, Abhinav |
| author_facet | Barua, Hrishav Bakul Stefanov, Kalin Krishnasamy, Ganesh Wong, KokSheik Dhall, Abhinav |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_16869 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PhysHDR: When Lighting Meets Materials and Scene Geometry in HDR Reconstruction Barua, Hrishav Bakul Stefanov, Kalin Krishnasamy, Ganesh Wong, KokSheik Dhall, Abhinav Graphics Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Multimedia Image and Video Processing Artificial intelligence, Computer vision, Machine learning, Deep learning I.3.3; I.4.5 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. |
| title | PhysHDR: When Lighting Meets Materials and Scene Geometry in HDR Reconstruction |
| topic | Graphics Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Multimedia Image and Video Processing Artificial intelligence, Computer vision, Machine learning, Deep learning I.3.3; I.4.5 |
| url | https://arxiv.org/abs/2509.16869 |