Low-light novel view synthesis is challenging because dark multi-view images contain noise, weak structural detail, and compressed dynamic range. Recent 3D Gaussian Splatting (3DGS) methods address these challenges by generating pseudo ground-truth (pseudo-GT) images as supervision targets when paired normal-light references are unavailable. Existing pseudo-GT methods apply a uniform linear gain to all pixels, which clips bright regions while providing insufficient enhancement in dark regions, limiting reconstruction quality. We observe that nonlinear tone mappings, long established in 2D low-light enhancement, have not been explored for pseudo-GT generation in 3D reconstruction. Accordingly, we propose a scene-adaptive nonlinear tone-curve framework that replaces linear pseudo-GT with nonlinear alternatives. The framework introduces percentile-based normalisation for scene-agnostic curve application, a scene-adaptive offset for automatic black-level adjustment, and two complementary curves: Adaptive SoftExp (ASE), a bounded exponential curve, and Adaptive Poly3 (AP3), a data-driven cubic polynomial. The module changes only the pseudo-GT computation and leaves the 3DGS backbone unchanged. Experiments on three benchmarks covering 21 scenes show that both curves consistently outperform the linear baseline with PSNR improvements up to +4.34 dB on LOM and +3.25 dB on RealX3D. Both curves achieve similar performance despite their different mathematical forms, suggesting the improvement is curve-agnostic. Code is available at https://github.com/lvmingzhe/adaptiveToneCurve

翻译：低光照新视角合成具有挑战性，因为暗光多视角图像包含噪声、弱结构细节和压缩的动态范围。最新的3D高斯泼溅（3DGS）方法通过生成伪真值图像作为监督目标来解决这些挑战，在无配对正常光照参考的情况下。现有伪真值方法对所有像素应用均匀线性增益，导致亮区域被截断，而暗区域增强不足，限制了重建质量。我们观察到，在二维低光照增强中已长期建立的非线性色调映射技术，尚未被探索用于三维重建中的伪真值生成。据此，我们提出了一种场景自适应非线性色调曲线框架，用非线性替代方案取代线性伪真值。该框架引入基于百分位数的归一化以实现场景无关的曲线应用、场景自适应偏移以自动调整黑电平，以及两条互补曲线：自适应SoftExp（ASE），一种有界指数曲线，和自适应Poly3（AP3），一种数据驱动的三次多项式。该模块仅改变伪真值计算，而不改动3DGS主干网络。在涵盖21个场景的三个基准上的实验表明，两条曲线均持续优于线性基线，在LOM和RealX3D上分别实现高达+4.34 dB和+3.25 dB的PSNR提升。尽管数学形式不同，两条曲线实现了相似的性能，表明改进与曲线类型无关。代码可在https://github.com/lvmingzhe/adaptiveToneCurve获取。