Unifying Color and Lightness Correction with View-Adaptive Curve Adjustment for Robust 3D Novel View Synthesis

TL;DR

This paper introduces Luminance-GS++, a novel 3D view synthesis framework that robustly corrects color and lightness variations caused by complex illumination, enhancing reconstruction and rendering quality in diverse lighting conditions.

Contribution

It presents a view-adaptive lightness correction method integrated with 3D Gaussian Splatting, preserving explicit 3D representations and enabling real-time, high-quality novel view synthesis under challenging illumination.

Findings

Achieves state-of-the-art results in low-light and overexposure scenarios.

Effectively maintains multi-view consistency in diverse lighting conditions.

Preserves real-time rendering efficiency with improved reconstruction fidelity.

Abstract

High-quality image acquisition in real-world environments remains challenging due to complex illumination variations and inherent limitations of camera imaging pipelines. These issues are exacerbated in multi-view capture, where differences in lighting, sensor responses, and image signal processor (ISP) configurations introduce photometric and chromatic inconsistencies that violate the assumptions of photometric consistency underlying modern 3D novel view synthesis (NVS) methods, including Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), leading to degraded reconstruction and rendering quality. We propose Luminance-GS++, a 3DGS-based framework for robust NVS under diverse illumination conditions. Our method combines a globally view-adaptive lightness adjustment with a local pixel-wise residual refinement for precise color correction. We further design unsupervised objectives that jointly enforce lightness correction and multi-view geometric and photometric consistency. Extensive experiments demonstrate state-of-the-art performance across challenging scenarios, including low-light, overexposure, and complex luminance and chromatic variations. Unlike prior approaches that modify the underlying representation, our method preserves the explicit 3DGS formulation, improving reconstruction fidelity while maintaining real-time rendering efficiency.