Physically Inspired Gaussian Splatting for HDR Novel View Synthesis

• URL: http://arxiv.org/abs/2603.28020v1
• Date: Mon, 30 Mar 2026 04:27:39 GMT
• Title: Physically Inspired Gaussian Splatting for HDR Novel View Synthesis
• Abstract summary: High dynamic range novel view synthesis (biased-NVS) reconstructs scenes with dynamic details by fusing multi-exposure low dynamic range (LDR) views.<n>Implicitly HDR content by constraining tone-mapped results fails in correcting abnormal HDR values, and results in limited gradients for Gaussians in under/over-exposed regions.<n>We introduce Phys-GS, a physically inspired HDR-NVS framework that models scene appearance via intrinsic reflectance and adjustable ambient illumination.
• Score: 40.99906396910274
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High dynamic range novel view synthesis (HDR-NVS) reconstructs scenes with dynamic details by fusing multi-exposure low dynamic range (LDR) views, yet it struggles to capture ambient illumination-dependent appearance. Implicitly supervising HDR content by constraining tone-mapped results fails in correcting abnormal HDR values, and results in limited gradients for Gaussians in under/over-exposed regions. To this end, we introduce PhysHDR-GS, a physically inspired HDR-NVS framework that models scene appearance via intrinsic reflectance and adjustable ambient illumination. PhysHDR-GS employs a complementary image-exposure (IE) branch and Gaussian-illumination (GI) branch to faithfully reproduce standard camera observations and capture illumination-dependent appearance changes, respectively. During training, the proposed cross-branch HDR consistency loss provides explicit supervision for HDR content, while an illumination-guided gradient scaling strategy mitigates exposure-biased gradient starvation and reduces under-densified representations. Experimental results across realistic and synthetic datasets demonstrate our superiority in reconstructing HDR details (e.g., a PSNR gain of 2.04 dB over HDR-GS), while maintaining real-time rendering speed (up to 76 FPS). Code and models are available at https://huimin-zeng.github.io/PhysHDR-GS/.

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