A 3D INDOOR-OUTDOOR BENCHMARK DATASET FOR LoD3 BUILDING POINT CLOUD SEMANTIC SEGMENTATION

Deep learning (DL) algorithms require high quality training samples as well as accurate and thorough annotations to work effectively. Up until now a limited number of datasets are available to train DL techniques for semantic segmentation of 3D building point clouds, except a few ones focusing on specific categories of constructions (e.g., cultural heritage buildings). This paper presents a new 3D Indoor/Outdoor building dataset (BIO dataset), which is aimed to provide a highly accurate, detailed, and comprehensive dataset to be used for applications related to sematic classification of buildings based on point clouds and meshes. This benchmark dataset contains 100 building models generated from existing polygonal models and belonging to different categories. These include commercial buildings, residential houses, industrial and institutional buildings. Structural elements of buildings are annotated into 11 semantic categories, following standards from IFC and CityGML. To verify the applicability of the BIO dataset for the semantic segmentation task, it has been successfully tested by using one machine learning technique and four different DL algorithms

Accurate Reconstruction of the LoD3 Building Model by Integrating Multi-Source Point Clouds and Oblique Remote Sensing Imagery

3D urban building models, which provide 3D information services for urban planning, management and operational decision-making, are essential for constructing digital cities. Unfortunately, the existing reconstruction approaches for LoD3 building models are insufficient in model details and are associated with a heavy workload, and accordingly they could not satisfy urgent requirements of realistic applications. In this paper, we propose an accurate LoD3 building reconstruction method by integrating multi-source laser point clouds and oblique remote sensing imagery. By combing high-precision plane features extracted from point clouds and accurate boundary constraint features from oblique images, the building mainframe model, which provides an accurate reference for further editing, is quickly and automatically constructed. Experimental results show that the proposed reconstruction method outperforms existing manual and automatic reconstruction methods using both point clouds and oblique images in terms of reconstruction efficiency and spatial accuracy