Existing Buildings Recognition and BIM Generation Based on Multi-Plane Segmentation and Deep Learning

Point cloud-based BIM reconstruction is an effective approach to enabling the digital documentation of existing buildings. However, current methods often demand substantial time and expertise for the manual measurement of building dimensions and the drafting of BIMs. This paper proposes an automated approach to BIM modeling of the external surfaces of existing buildings, aiming to streamline the labor-intensive and time-consuming processes of manual measurement and drafting. Initially, multi-angle images of the building are captured using drones, and the building’s point cloud is reconstructed using 3D reconstruction software. Next, a multi-plane segmentation technique based on the RANSAC algorithm is applied, facilitating the efficient extraction of key features of exterior walls and planar roofs. The orthophotos of the building façades are generated by projecting wall point clouds onto a 2D plane. A lightweight convolutional encoder–decoder model is utilized for the semantic segmentation of windows and doors on the façade, enabling the precise extraction of window and door features and the automated generation of AutoCAD elevation drawings. Finally, the extracted features and segmented data are integrated to generate the BIM. The case study results demonstrate that the proposed method exhibits a stable error distribution, with model accuracy exceeding architectural industry requirements, successfully achieving reliable BIM reconstruction. However, this method currently faces limitations in dealing with buildings with complex curved walls and irregular roof structures or dense vegetation obstacles.

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