SkyLume: A Large-Scale Multi-Illumination Aerial Benchmark for Urban Scene Reconstruction and Beyond

Recent advances in Neural Radiance Fields and 3D Gaus-sian Splatting have demonstrated immense potential for large-scale UAV-based 3D reconstruction. However, real-world data collection often spansmultiple times of the day, violating the fundamental photometric consis-tency assumption. Such multi-temporal illumination inconsistencies in-evitably lead to severe color artifacts, coupled shadows, and degradedgeometric accuracy. Due to the lack of UAV datasets that systematicallycapture the same areas under varying lighting conditions, this crucialchallenge remains largely underexplored. To bridge this gap, we introduceSkyLume, a large-scale, real-world UAV dataset specifically designed forstudying illumination-robust 3D reconstruction and urban scene model-ing. Our dataset features three primary contributions: (1) We collect over100k high-resolution UAV images (nadir and four oblique views) span-ning 10 diverse urban regions, with each region meticulously capturedacross three distinct time periods to systematically isolate illuminationvariations. (2) To enable rigorous evaluation, we provide comprehensiveper-scene LiDAR scans, offering highly accurate ground truth for as-sessing depth, surface normals, and geometric fidelity. (3) For inverserendering and appearance decoupling tasks, we introduce the Tempo-ral Consistency Coefficient (TCC), a novel metric designed to quantifycross-time rendering stability. We envision SkyLume as a foundationalbenchmark that will advance research and real-world evaluation in large-scale inverse rendering, robust geometry reconstruction, and novel viewsynthesis. Project Page: skylume.