P2GS: Physical Prior-guided Gaussian Splatting for Photometrically Consistent Urban Reconstruction

2026-05-16

AI Summary

This work addresses the photometric inconsistencies and artifacts in static backgrounds caused by varying camera exposures and dynamic illumination in real-world driving scenarios when using conventional 3D Gaussian splatting. To resolve this, we propose the first Gaussian splatting framework that integrates a physical imaging model, enabling joint disentanglement of a view-invariant linear HDR radiance field, per-view exposure scales, and tone-mapping functions from only LDR images. By incorporating relative exposure consistency constraints and HDR-domain radiance regularization, our method achieves cross-view photometrically consistent reconstruction without requiring HDR supervision. Evaluated on both real and synthetic driving datasets, the approach attains state-of-the-art LDR reconstruction quality while significantly improving photometric consistency, reliability of exposure normalization, and physical plausibility of recovered illumination.

Abstract

3D Gaussian Splatting (3DGS) has recently emerged as a powerful explicit representation enabling fast, high-fidelity rendering, making it a promising foundation for closed-loop simulators and perception models in autonomous driving. However, conventional 3DGS implicitly assumes consistent exposure and tone mapping across views. Real driving data violates this assumption due to heterogeneous camera pipelines and dynamic outdoor illumination, baking exposure discrepancies and sensor noise into the radiance field and producing artifacts and inconsistent illumination especially in static backgrounds crucial for realistic simulation. These issues are amplified in autonomous driving, where sparse viewpoints, varying exposures, and outdoor lighting interact, while prior work mainly targets dynamic-object reconstruction and overlooks cross-view photometric consistency. To address this limitation, we introduce P2GS, a physically consistent Gaussian Splatting framework that jointly decomposes a view-invariant linear HDR radiance field, per-view exposure scales, and tone-mapping functions from only LDR images without HDR supervision. P2GS employs a unified optimization strategy grounded in the physical image-formation process, enforcing relative-exposure consistency and HDR-domain radiance regularization. This yields a radiance field robust to inter-camera illumination differences while preserving the real-time efficiency of standard 3DGS. Experiments across real and simulated driving environments show that P2GS matches or surpasses prior methods in LDR reconstruction while providing substantially improved photometric consistency, reliable exposure normalization, and physically coherent illumination across diverse scenes.

Problem

• photometric consistency
• 3D Gaussian Splatting
• exposure variation
• urban reconstruction
• HDR radiance field

Innovation

• Gaussian Splatting
• Photometric Consistency
• HDR Radiance Field
• Exposure Decomposition
• Physical Image Formation