PAM-ToD: Plug-and-Play Appearance Modeling for Cross-Time-of-Day 3D Gaussian Splatting

2026-10-08

AI Summary

This study addresses the challenge of adapting pre-trained 3D Gaussian Splatting models to cross-temporal scenes, where maintaining appearance consistency and real-time rendering with limited anchor images remains difficult. To overcome this, the authors propose a lightweight, plug-and-play module that decouples illumination and brightness variations through color scaling and additive terms. By leveraging a simplified imaging model to eliminate albedo dependency and introducing spatial smoothness constraints to guide few-shot learning, the method achieves cross-temporal appearance correction without fixed parameters. Furthermore, this work establishes the CARLA-ToD benchmark dataset. Experimental results demonstrate that, using only single-temporal multi-view anchor images, the proposed approach significantly outperforms baselines in PSNR and LPIPS across both static and dynamic scenes, enabling high-quality, real-time cross-temporal novel view synthesis.

Abstract

Adapting a pre-trained 3D Gaussian Splatting (3DGS) road scene to a new time of day requires learning appearance changes from a few anchor images while preserving consistent, real-time rendering. We propose PAM-ToD, a lightweight plug-in that learns color corrections while keeping the pre-trained 3DGS parameters fixed. PAM-ToD scales each Gaussian's existing color to model illumination changes and uses an additive term for additional brightness, such as when street lamps turn on at night. Under a simplified image formation model, unchanged surface albedo can be eliminated from the relation between source and target appearances, allowing us to learn these corrections without separately estimating albedo and illumination. The model corrects colors across the scene while allowing the corrections to vary by location and by Gaussian. To guide learning from a few anchor images, it discourages abrupt spatial changes in these corrections. We also introduce CARLA-ToD, a benchmark with matching geometry, camera poses, and moving-object trajectories across three times of day. A few target-time anchor images are used to train each plug-in, while separate views are used for evaluation. Across the static and dynamic settings, PAM-ToD achieves higher PSNR and lower LPIPS than the baselines, even when the anchor images come from a single synchronized capture across multiple cameras.

Problem

• 3D Gaussian Splatting
• Cross-Time-of-Day
• Appearance Modeling
• Road Scene
• Few-shot Adaptation

Innovation

• 3D Gaussian Splatting
• Cross-Time-of-Day
• Plug-and-Play Appearance Modeling
• Image Formation Model
• Few-shot Adaptation