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MSc Thesis - TU Delft - Building Opening Detection in Urban Point Clouds - Willem van der Vliet

MSc Thesis by Willem van der Vliet

Digital twins (DTs) are a common way for city planners and citizens to visualize impact of new policy decisions, simulate scenarios, and plan for disasters. The more detail these DTs have, the more useful they can be. Windows and doors, also known as building openings, are a critical detail missing in Amsterdam’s DT. While previous works use images, this paper investigates the usefulness of a lidar-based point cloud for building opening detection by proposing a pipeline to this end.

This paper creates a dataset for semantic segmentation and a dataset for instance segmentation for window and door classes. We begin with isolating individual buildings from larger scans using their footprint accessed from a government building database. Then, each building cloud is passed to a RandLA-Net neural network for point-wise semantic segmentation. After inference, the cloud passes through an internal false positive rejection (IFPR) module that makes use of the building footprint to rectify incorrectly labeled points in the building’s interior. Then, points are clustered into instances by an adaptive DBSCAN algorithm that derives information from data inherent to each point cloud. This pipeline shows promising results on a small-scale dataset, detecting window and door points with a mIoU of 37.81, and 40.57, respectively. Post-processing yields a 3D IoU score of 77.1 for windows and 81.32 for doors in the ideal inference case.

This paper tackles the main challenges of lidar-based building opening detection and discusses ways to mitigate these challenges in future work.

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