3D Lookup Tables (3D LUTs) are widely used for color mapping, but their grid-based representation requires discretizing the RGB space, leading to a capacity-memory trade-off that becomes prohibitive when storing large numbers of LUTs. Recent approaches adopt implicit neural representations to improve scalability, yet their black-box nature limits interpretability and hinders intuitive, localized editing. In this paper, we propose Gaussian LUT (GLUT), a continuous and explicit color representation that models color transformations using a set of learnable 3D Gaussian primitives. By avoiding fixed-resolution grids, GLUT achieves flexible representational capacity while maintaining a compact memory footprint. Its explicit, spatially localized formulation further enables both accurate modeling and interpretability. Building on this representation, we introduce a compact conditional generator (CGLUT) that predicts GLUT parameters for multiple LUT instances, encoding diverse color styles in a single framework to enable smooth and controllable LUT style blending. Moreover, GLUT supports efficient, user-friendly editing by allowing localized adjustments to specific color regions without global retraining. Experimental results demonstrate that our approach outperforms prior neural LUT representations in both accuracy and efficiency, while offering improved interpretability and interactive control.

翻译：三维查找表（3D LUT）广泛用于色彩映射，但其基于网格的表示方式需要对RGB空间进行离散化，导致容量与内存之间的权衡，这在存储大量LUT时变得不切实际。近期方法采用隐式神经表示以提高可扩展性，但其黑箱特性限制了可解释性，并阻碍了直观的局部编辑。在本文中，我们提出高斯查找表（GLUT），一种连续且显式的颜色表示方法，利用一组可学习的三维高斯原语对颜色变换进行建模。GLUT通过避免固定分辨率网格，在保持紧凑内存占用的同时实现了灵活的表征容量。其显式、空间局部化的公式进一步实现了精确建模与可解释性。基于该表示，我们引入了一个紧凑的条件生成器（CGLUT），用于预测多个LUT实例的GLUT参数，在统一框架中编码多样化的颜色风格，从而实现平滑且可控的LUT风格融合。此外，GLUT支持高效、用户友好的编辑，允许对特定颜色区域进行局部调整，而无需全局重新训练。实验结果表明，我们的方法在准确性和效率上均优于先前的神经LUT表示，同时提供了更强的可解释性和交互控制能力。