P2GS：物理先验引导的高斯泼溅用于光度一致的城市重建
P2GS: Physical Prior-guided Gaussian Splatting for Photometrically Consistent Urban Reconstruction
把逐视角曝光尺度与色调映射函数作为可优化变量，与线性 HDR 辐射场联合分解，可在无 HDR 监督下消除多相机驾驶数据中的跨视角光度不一致，对做街景重建与闭环仿真的人可直接复用。
原文依据 jointly decomposes a view-invariant linear HDR radiance field, per-view exposure scales, and tone-mapping functions from only LDR images
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论3D 高斯泼溅（3DGS）近来已成为一种强大的显式表示，能够实现快速、高保真的渲染，因而有望成为自动驾驶中闭环仿真器与感知模型的基础。然而，传统 3DGS 隐含地假设各视角之间具有一致的曝光与色调映射。真实驾驶数据由于相机流水线异构以及户外光照动态变化而违背这一假设，从而将曝光差异与传感器噪声烘焙进辐射场，产生伪影与不一致的光照，这在对于真实感仿真至关重要的静态背景中尤为明显。在自动驾驶场景中，稀疏视角、变化的曝光与户外光照相互作用，使这些问题被进一步放大，而先前工作主要针对动态物体重建，忽视了跨视角的光度一致性。为解决这一局限，我们提出 P2GS，一个物理一致的高斯泼溅框架，仅从 LDR 图像出发、无需 HDR 监督，即可联合分解出视角无关的线性 HDR 辐射场、逐视角曝光尺度与色调映射函数。P2GS 采用基于物理成像过程的统一优化策略，施加相对曝光一致性约束与 HDR 域辐射正则化。由此得到的辐射场对相机间光照差异具有鲁棒性，同时保持标准 3DGS 的实时效率。在真实与仿真驾驶环境中的实验表明，P2GS 在 LDR 重建上达到或超越先前方法，同时在多样场景中提供显著改善的光度一致性、可靠的曝光归一化与物理上连贯的光照。
英文原文摘要 1739 字符
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.