An adaptive down-sampling method of laser scan data for scan-to-BIM

Laser scan data are popularly adopted to capture the conditions of existing buildings for as-is BIM reconstruction, also known as scan-to-BIM. Down-sampling of massive laser scan data is a critical pre-processing step for efficient scan-to-BIM. It is also crucial to accurately retain the geometric and semantic information of existing buildings during the down-sampling process. However, existing down-sampling approaches mainly focus on the preservation of geometric features only, without considering semantic features. This study developed an adaptive down-sampling method, which is able to maintain scan points containing critical geometric or semantic information and only down-sample scan points containing no critical information. The proposed method first conducts a geometry-based segmentation to identify edge points and non-planar points, which contain critical geometric information. Then, a semantic-based segmentation is performed to identify points with critical semantic information (e.g. labels and information boards). All the points containing either geometric or semantic information are retained, while points containing no critical information are down-sampled to reduce data redundancy. Experiments were conducted on four scenes, which showed that the proposed method was suitable for point cloud data with different accuracies and could achieve a better performance in preserving both geometric and semantic information than the traditional voxel-based method and the curvature-based method. It is proved that down-sampled data with the proposed method could generate as-is BIMs with more accurate geometric and semantic information.