# Naka-GS: Naka-Guided Chroma Correction and Point-Prior Compaction for Low-Light 3D Gaussian Splatting

arXiv: https://arxiv.org/abs/2604.11142 · HTML: https://mlsanity.com/paper/2604.11142/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 24 references that are in the map.
- First posted on arXiv 13 Apr 2026.

## Builds on (most cited first, 18 of 18)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [VGGT: Visual Geometry Grounded Transformer](https://mlsanity.com/paper/2503.11651/) (2025)
- [Deep Retinex Decomposition for Low-Light Enhancement](https://mlsanity.com/paper/1808.04560/) (2018)
- [Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement](https://mlsanity.com/paper/2012.05609/) (2020)
- [Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement](https://mlsanity.com/paper/2303.06705/) (2023)
- [Zero-Reference Low-Light Enhancement via Physical Quadruple Priors](https://mlsanity.com/paper/2403.12933/) (2024)
- [Aleth-NeRF: Illumination Adaptive NeRF with Concealing Field Assumption](https://mlsanity.com/paper/2312.09093/) (2023)
- [Lighting Every Darkness with 3DGS: Fast Training and Real-Time Rendering for HDR View Synthesis](https://mlsanity.com/paper/2406.06216/) (2024)
- [Luminance-GS: Adapting 3D Gaussian Splatting to Challenging Lighting Conditions with View-Adaptive Curve Adjustment](https://mlsanity.com/paper/2504.01503/) (2025)
- [DarkGS: Learning Neural Illumination and 3D Gaussians Relighting for Robotic Exploration in the Dark](https://mlsanity.com/paper/2403.10814/) (2024)
- [LITA-GS: Illumination-Agnostic Novel View Synthesis via Reference-Free 3D Gaussian Splatting and Physical Priors](https://mlsanity.com/paper/2504.00219/) (2025)
- [Gaussian in the Dark: Real-Time View Synthesis From Inconsistent Dark Images Using Gaussian Splatting](https://mlsanity.com/paper/2408.09130/) (2024)
- [RealX3D: A Physically-Degraded 3D Benchmark for Multi-view Visual Restoration and Reconstruction](https://mlsanity.com/paper/2512.23437/) (2025)
- [I2-NeRF: Learning Neural Radiance Fields Under Physically-Grounded Media Interactions](https://mlsanity.com/paper/2510.22161/) (2025)
- [LuSh-NeRF: Lighting up and Sharpening NeRFs for Low-light Scenes](https://mlsanity.com/paper/2411.06757/) (2024)
- [From Chaos to Clarity: 3DGS in the Dark](https://mlsanity.com/paper/2406.08300/) (2024)
- [LL-Gaussian: Low-Light Scene Reconstruction and Enhancement via Gaussian Splatting for Novel View Synthesis](https://mlsanity.com/paper/2504.10331/) (2025)
- [Unifying Color and Lightness Correction with View-Adaptive Curve Adjustment for Robust 3D Novel View Synthesis](https://mlsanity.com/paper/2602.18322/) (2026)

## Built on by (most cited first, 2 of 2)

- [ELoG-GS: Dual-Branch Gaussian Splatting with Luminance-Guided Enhancement for Extreme Low-light 3D Reconstruction](https://mlsanity.com/paper/2604.12592/) (2026)
- [Dehaze-then-Splat: Generative Dehazing with Physics-Informed 3D Gaussian Splatting for Smoke-Free Novel View Synthesis](https://mlsanity.com/paper/2604.13589/) (2026)

## Abstract (arXiv)

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