GLUT: 3D Gaussian Lookup Table for Continuous Color Transformation

TL;DR

GLUT introduces a continuous, explicit 3D Gaussian-based color transformation method that enhances interpretability, efficiency, and user control over color mapping compared to traditional grid-based and neural approaches.

Contribution

The paper proposes GLUT, a novel Gaussian primitive-based color transformation model, and CGLUT, a compact generator for diverse, controllable LUTs, improving interpretability and editing capabilities.

Findings

Outperforms prior neural LUTs in accuracy and efficiency.

Enables localized, user-friendly color adjustments.

Supports smooth style blending across multiple LUTs.

Abstract

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.