让机器人在暗光下重建3D场景时自动补光，效果更真实。
SpotlessGS: Relightable 3D Gaussian Splatting under Dynamic Illumination for Robotic Perception
- 在3D高斯点云中联合优化光照参数，无需手动标定光源。
- 用球谐函数建模空间变化的环境光，减少阴影不均。
- 加入MLP BRDF模型，更好还原反光材质，适合做机器人感知。
在黑暗或照明不足的环境中，依赖车载光源的机器人常因光照不均导致下游感知性能下降。现有基于2D图像增强的方法缺乏可靠监督且难以保持多视角几何一致性。为此，我们扩展了暗光高斯点云（DarkGS）框架，构建更精确灵活的可重光照3D重建方法：首先，在高斯点云框架内联合优化光照参数，避免显式光源标定；其次，引入基于球谐函数（SH）的低频光照模型，捕捉空间变化的残余光与环境光效应；第三，采用基于MLP的双向反射分布函数（BRDF）建模非朗伯反射特性。在合成及真实数据集上的实验表明，该方法有效缓解光照伪影，提升渲染质量与量化指标表现。进一步通过下游任务验证了其对机器人感知的增益。
原文摘要 · Abstract (English)
Robots operating in dark or poorly lit environments rely on onboard lights, which often produce uneven illumination that degrades downstream perception tasks. Prior approaches based on 2D image enhancement lack reliable supervision and fail to preserve multi-view geometric consistency. To address these limitations, we extend Dark Gaussian Splatting (DarkGS) toward a more accurate and flexible relightable 3D reconstruction framework. First, we eliminate the need for explicit light parameter calibration by jointly optimizing lighting parameters within the Gaussian Splatting framework. Second, we introduce a low-frequency illumination model based on spherical harmonics (SH) to capture spatially varying residual and ambient lighting effects. Third, we incorporate an MLP-based Bidirectional Reflectance Distribution Function (BRDF) to model non-Lambertian reflectance. Experiments on synthetic and real-world datasets demonstrate that our method effectively mitigates illumination artifacts while improving rendering quality and quantitative performance over prior approaches. We further validate its benefits for robotic perception through a downstream task.
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