发表机构
Technical University of Munich; ETH Zurich（慕尼黑工业大学; 苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对机器人在差光照环境下感知性能下降问题，提出SpotlessGS方法，通过优化光照参数、引入SH光照模型及MLP-BRDF，实现可重光照三维重建，提升了渲染与感知性能。
AI 中文摘要
在黑暗或光照不佳环境中作业的机器人依赖机载灯光，而这类灯光常产生不均匀光照，导致下游感知任务性能下降。基于二维图像增强的现有方法缺乏可靠监督，且无法保留多视图几何一致性。为解决这些局限，我们将Dark Gaussian Splatting（DarkGS）扩展为更精准灵活的可重光照三维重建框架：其一，我们在高斯溅射框架内联合优化光照参数，无需显式光照参数校准；其二，我们引入基于球谐函数（SH）的低频光照模型，以捕捉空间变化的残差与环境光照效应；其三，我们融入基于多层感知机（MLP）的双向反射分布函数（BRDF），用于建模非朗伯反射。在合成与真实数据集上的实验表明，我们的方法可有效缓解光照伪影，同时相较现有方法提升了渲染质量与定量性能，我们还通过一项下游任务验证了其对机器人感知的益处。
英文摘要
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.
CommentsAccepted to the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026)
