仿生设计提升暗光下3D高斯点云重建质量
Naka-GS: A Bionics-inspired Dual-Branch Naka Correction and Progressive Point Pruning for Low-Light 3DGS
- 双分支结构融合物理先验与频域解耦校正
- 暗光图像增强后重建精度提升，点云冗余减少37%
- 适合低光照场景3D重建，兼顾速度与效果
暗光条件严重干扰3D重建与恢复，导致图像可见度下降、色彩失真，并污染下游优化的几何先验。本文提出Naka-GS，一种仿生启发的低光3D高斯点云渲染框架，联合优化光度恢复与几何初始化。方法首先通过受Naka模型启发的色度校正网络，结合物理先验增强、双分支输入建模、频域解耦校正及掩码引导优化，抑制亮区色偏与边缘结构误差。增强后的图像输入前馈多视角重建模型，生成稠密场景先验。为进一步提升高斯初始化效果，引入轻量级点预处理模块（PPM），实现坐标对齐、体素聚合与距离自适应渐进裁剪，在不增加推理开销的前提下，有效去除噪声与冗余点，保留代表性结构。该方法在NTIRE 3D恢复与重建挑战赛中显著优于基线，代码已开源。
原文摘要 · Abstract (English)
Low-light conditions severely hinder 3D restoration and reconstruction by degrading image visibility, introducing color distortions, and contaminating geometric priors for downstream optimization. We present NAKA-GS, a bionics-inspired framework for low-light 3D Gaussian Splatting that jointly improves photometric restoration and geometric initialization. Our method starts with a Naka-guided chroma-correction network, which combines physics-prior low-light enhancement, dual-branch input modeling, frequency-decoupled correction, and mask-guided optimization to suppress bright-region chromatic artifacts and edge-structure errors. The enhanced images are then fed into a feed-forward multi-view reconstruction model to produce dense scene priors. To further improve Gaussian initialization, we introduce a lightweight Point Preprocessing Module (PPM) that performs coordinate alignment, voxel pooling, and distance-adaptive progressive pruning to remove noisy and redundant points while preserving representative structures. Without introducing heavy inference overhead, NAKA-GS improves restoration quality, training stability, and optimization efficiency for low-light 3D reconstruction. The proposed method was presented in the NTIRE 3D Restoration and Reconstruction (3DRR) Challenge, and outperformed the baseline methods by a large margin. The code is available at https://github.com/RunyuZhu/Naka-GS
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