GT-LOD3: LOD3 Semantic 3D Building Reconstruction Benchmark Dataset

Abstract. Reconstructing semantic 3D building models at level of detail 3 (LOD3) is a long-standing challenge in photogrammetry, remote sensing, and computer vision. In contrast to conventional mesh-based representations, LOD3 models require watertight geometries along with semantic object-level facade elements. In GT-LOD3, we introduce the first LOD3 building benchmark dataset comprising point clouds and images paired with 32 ground truth (GT) LOD3 instances in two different countries. We also analyze the performance of selected baselines, complemented by a discussion on unresolved challenges. We are convinced that GT-LOD3 will facilitate the development of novel LOD3 reconstruction methods, enabling the widespread adoption of LOD3 models and, consequently, various downstream applications, ranging from energy demand estimation to automated driving function testing. We release the dataset as open-source and it can be accessed at https://github.com/gdslab/GT-LOD3-Benchmark