SplatBright: 从稀疏视角进行通用低光照场景重建的物理引导高斯增强
SplatBright: Generalizable Low-Light Scene Reconstruction from Sparse Views via Physically-Guided Gaussian Enhancement
AI总结：
SplatBright通过物理引导的照明建模和几何-外观解耦，实现了从稀疏视角进行低光照场景重建的通用3D高斯框架。
摘要：
低光照条件下从稀疏视角进行3D重建仍然具有挑战性，因为存在曝光不平衡和颜色保真度降低的问题。尽管现有方法在视角不一致和需要每场景训练方面存在困难，我们提出了SplatBright，据我们所知，这是第一个通用的3D高斯框架，用于从稀疏sRGB输入中联合进行低光照增强和重建。我们的关键思想是将物理引导的照明建模与几何-外观解耦相结合，以实现一致的低光照重建。具体而言，我们采用了一个双分支预测器，为3D高斯参数提供稳定的几何初始化。在外观方面，照明一致性利用频率先验来实现可控且跨视角一致的照明，而外观细化模块进一步分离照明、材料和视角相关的线索以恢复精细纹理。为解决缺乏大规模几何一致配对数据的问题，我们通过基于物理的相机模型合成暗视图用于训练。在公共和自收集的数据集上进行了广泛的实验，证明SplatBright在新视角合成、跨视角一致性和对未见低光照场景的泛化能力方面优于2D和3D方法。
英文摘要：
Low-light 3D reconstruction from sparse views remains challenging due to exposure imbalance and degraded color fidelity. While existing methods struggle with view inconsistency and require per-scene training, we propose SplatBright, which is, to our knowledge, the first generalizable 3D Gaussian framework for joint low-light enhancement and reconstruction from sparse sRGB inputs. Our key idea is to integrate physically guided illumination modeling with geometry-appearance decoupling for consistent low-light reconstruction. Specifically, we adopt a dual-branch predictor that provides stable geometric initialization of 3D Gaussian parameters. On the appearance side, illumination consistency leverages frequency priors to enable controllable and cross-view coherent lighting, while an appearance refinement module further separates illumination, material, and view-dependent cues to recover fine texture. To tackle the lack of large-scale geometrically consistent paired data, we synthesize dark views via a physics-based camera model for training. Extensive experiments on public and self-collected datasets demonstrate that SplatBright achieves superior novel view synthesis, cross-view consistency, and better generalization to unseen low-light scenes compared with both 2D and 3D methods.
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