Low-light inputs obscure scene content, distort color, and corrupt geometric priors, substantially degrading 3D Gaussian Splatting. Although image enhancement can recover visibility, it often introduces chromatic deviations and fine-scale artifacts that propagate through multi-view reconstruction. Moreover, dense point priors can further amplify these errors and increase optimization cost. To address the coupled challenges, we present Naka-GS, a low-light 3DGS framework that jointly improves observations and Gaussian initialization while retaining the standard 3DGS renderer and training objective. Our Naka-Guided Chroma Correction treats a fixed bionic response as a photometric anchor and learns bounded, frequency-decoupled luminance and chromatic corrections, preserving fine structures while suppressing residual color and boundary artifacts. The corrected views are reconstructed into dense scene priors, which a lightweight, non-learned Point-Prior Compaction Module refines through voxel pooling and distance-adaptive progressive pruning. On eight scene-disjoint RealX3D scenes, Naka-GS achieves 20.735~dB PSNR, 0.7408 SSIM, and 0.3340 LPIPS. PPM reduces initialization from 1.00M to 59.52K points and shortens average 3DGS optimization time by 37.7%.The code is available at https://github.com/RunyuZhu/Naka-GS

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