eRetinexGS：通过事件流与 3D 高斯泼溅进行 Retinex 建模的低光照场景增强
eRetinexGS: Retinex Modeling for Low-Light Scene Enhancement via Event Streams and 3D Gaussian Splatting
把 Retinex 的反射率-光照分解嵌入 3DGS 的辐射场优化，并用事件流提供高动态范围几何/光度约束，为低光照新视角合成与场景重建提供了一套可复用的多视图一致性框架。
原文依据 introducing an event-assisted Retinex decomposition and a reflectance-illumination representation within the 3DGS pipeline
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论低照度下的感知仍是计算机视觉系统的一大挑战，因为 RGB 传感器在极暗环境中往往无法捕获足够的结构与颜色信息。事件相机凭借其高动态范围与时序分辨率，能够提供非常适合此类条件的互补线索。在本工作中，我们提出 eRetinexGS，这是一个通过 3D 高斯泼溅联合利用事件流与低光照帧、实现场景级增强与重建的新颖框架。与以往在单帧上操作的方法不同，eRetinexGS 在多个视图之间施加几何与光度一致性约束，弥合了退化图像与含噪事件信号之间的差距。通过在 3DGS 流程中引入事件辅助的 Retinex 分解以及反射率-光照表示，我们的方法重建出细节精细、颜色准确的正光辐射场。在合成与真实数据集上的大量实验表明，eRetinexGS 在低光照场景增强方面达到最优性能，同时保持实时渲染能力。
英文原文摘要 1171 字符
Perception under low illumination remains a major challenge for computer vision systems, as RGB sensors often fail to capture sufficient structural and color information in extremely dark environments. Event cameras, with their high dynamic range and temporal resolution, provide complementary cues that are well suited for such conditions. In this work, we present eRetinexGS, a novel framework that jointly leverages event streams and low-light frames through 3D Gaussian Splatting for scene-level enhancement and reconstruction. Unlike previous approaches that operate on individual frames, eRetinexGS enforces geometric and photometric consistency across multiple views, bridging the gap between degraded images and noisy event signals. By introducing an event-assisted Retinex decomposition and a reflectance-illumination representation within the 3DGS pipeline, our method reconstructs normal-light radiance fields with fine-grained details and accurate color. Extensive experiments on both synthetic and real datasets demonstrate that eRetinexGS achieves state-of-the-art performance in low-light scene enhancement while maintaining real-time rendering capability.