Object Surface-Based Predictive View Planning for Scan-to-BIM: A Cost-Effective Method for Generating BIM of Existing Buildings from Point Cloud Data

Abstract

Extracting a building information model (BIM) from point cloud data of an existing structure is referred to as scan-to-BIM. It is crucial for effective renovation planning, progress monitoring, and life-cycle asset management. Terrestrial close-range photogrammetry is a cost-effective data acquisition technique for scan-to-BIM, with view planning being a critical preparatory step. However, traditional view planning often relies on iterative, experience-based camera placements, leading to inefficiencies, potential data gaps, and increased project costs until a satisfactory point cloud is acquired. This study systematically optimizes view planning by assessing the object surface photogrammetric information (OSPI) generated from multiview images before field data acquisition. Based on camera-building geometry, the method categorizes potential camera positions into stereo, transition, and convergent zone at the center (CZC). By calculating an OSPI value for points on the building surface, considering constraints such as uniform resolution and convergence angles, the method enables informed decision-making on camera network design. A minimum OSPI threshold helps determine the optimal number and distribution of camera positions. For instance, to achieve a level of detail 1 (LoD1) BIM for a 90° corner, the study found that two images in the CZC, three in each transition zone, and normal images with 90% overlap provided an accurate point cloud. Similarly, for a more detailed LoD2 BIM, the requirements increased to six images in CZC and five additional normal images in stereo zone. The results of the study were validated on a large-scale building (1,033 images) by developing a BIM model from the point cloud data with an accuracy of 2 cm. Factors influencing cost are discussed. This low-cost, predictive view planning (PVP) approach empowers project managers and BIM coordinators to make strategic decisions on camera placement, minimizing rework, optimizing field time, and ensuring the generation of point clouds fit for scan-to-BIM purposes.