Capturing high-quality photographs across diverse real-world lighting conditions is challenging, as both natural lighting (e.g., low-light) and camera exposure settings (e.g., exposure time) strongly influence image quality. This difficulty intensifies in multi-view scenarios, where each viewpoint can have distinct lighting and image signal processor (ISP) settings, causing photometric inconsistencies between views. These lighting degradations and view variations significant challenges to both NeRF- and 3D Gaussian Splatting (3DGS)-based novel view synthesis (NVS) frameworks.To address this, we introduce Luminance-GS, a novel approach to achieve high-quality novel view synthesis results under diverse and challenging lighting conditions using 3DGS. By adopting per-view color space mapping and view adaptive curve adjustments, Luminance-GS achieves state-of-the-art (SOTA) results across various lighting conditions—including low-light, overexposure, and varying exposure—without altering the original 3DGS explicit representation. Compared to previous NeRF- and 3DGS-based baselines, Luminance-GS provides real-time rendering speed with improved reconstruction quality. We would release the source code.