SpotlessGS: 動的照明下でのロボット知覚のための再照明可能な3Dガウシアンスプラッティング
SpotlessGS: Relightable 3D Gaussian Splatting under Dynamic Illumination for Robotic Perception
暗所でロボットの照明が不均一になる問題を解決するため、3Dガウシアンスプラッティングに照明パラメータの同時最適化、球面調和関数による低周波照明モデル、MLPベースのBRDFを導入し、再照明可能な3D再構成を実現した。
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著者: Liang Hong, Jiaxin Wei, Simon Schaefer, Stefan Leutenegger, Jaehyung Jung
分類: cs.RO, cs.CV
原文アブストラクト
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.