City-Facade: A city-level large-scale point cloud building facade dataset for semantic & instance segmentation

With the growing demand for high-quality 3D urban scene understanding in applications such as building information modeling (BIM) and digital twins, large-scale and well-annotated 3D datasets have become essential for advancing scientific research and algorithm development. However, existing building facade datasets are predominantly image-based, suffering from drawbacks such as a lack of spatial information and sensitivity to lighting and weather conditions. Moreover, publicly available large-scale labeled datasets of building point clouds still remain scarce and have a relatively small coverage area. To this end, we introduce a city-level building facade point cloud dataset named City-Facade for semantic-level and instance-level segmentation. Firstly, the paper conducts a comprehensive review and analysis of existing urban & building point cloud datasets and point cloud segmentation algorithms. Secondly, we present a large-scale building facade dataset with approximately 200 millions of labeled 3D point clouds (over 60 km roads) belonging to urban scenarios, realized to facilitate the development and evaluation of semantic and instance level algorithms in the urban understanding. Finally, baseline experiments for semantic and instance segmentation are conducted to encourage further research. The proposed dataset is accessible at https://github.com/gorgeouseping/City-Facade , comprising the dataset and segmentation baselines for better comparison and presentation of strengths and weaknesses of different methods. Additionally, the data will undergo continuous improvement and updates based on feedback from the community.