Multi-traversal scene reconstruction is important for high-fidelity autonomous driving simulation and digital twin construction. This task involves integrating multiple sequences captured from the same geographical area at different times. In this context, a primary challenge is the significant appearance inconsistency across traversals caused by varying illumination and environmental conditions, despite the shared underlying geometry. This paper presents ADM-GS (Appearance Decomposition Gaussian Splatting for Multi-Traversal Reconstruction), a framework that applies an explicit appearance decomposition to the static background to alleviate appearance entanglement across traversals. For the static background, we decompose the appearance into traversal-invariant material, representing intrinsic material properties, and traversal-dependent illumination, capturing lighting variations. Specifically, we propose a neural light field that utilizes a frequency-separated hybrid encoding strategy. By incorporating surface normals and explicit reflection vectors, this design separately captures low-frequency diffuse illumination and high-frequency specular reflections. Quantitative evaluations on the Argoverse 2 and Waymo Open datasets demonstrate the effectiveness of ADM-GS. In multi-traversal experiments, our method achieves a +0.98 dB PSNR improvement over existing latent-based baselines while producing more consistent appearance across traversals. Code will be available at https://github.com/IRMVLab/ADM-GS.

翻译：多轨迹场景重建对于高保真度自动驾驶仿真和数字孪生构建具有重要意义。该任务需整合同一地理区域在多个不同时间段采集的序列数据。在此背景下，尽管共享底层几何结构，各轨迹间因光照和环境条件差异导致的显著外观不一致性成为主要挑战。本文提出ADM-GS（面向多轨迹重建的外观分解高斯泼溅）框架，通过对静态背景进行显式外观分解来缓解轨迹间外观耦合问题。针对静态背景，我们将外观分解为轨迹不变材质（表征固有材料属性）与轨迹相关光照（捕捉光照变化）两部分。具体而言，我们提出一种采用频率分离混合编码策略的神经光场，通过引入表面法向量与显式反射向量，该设计可分别捕捉低频漫反射光照与高频镜面反射特征。在Argoverse 2和Waymo Open数据集上的定量评估验证了ADM-GS的有效性。在多轨迹实验中，本方法在实现更一致轨迹间外观的同时，相比现有基于隐变量的基线方法实现了+0.98 dB的PSNR提升。代码将发布于https://github.com/IRMVLab/ADM-GS。