Productive high-complexity 3D city modeling with point clouds collected from terrestrial LiDAR

Abstract

To satisfy the needs of photo-realistic and ground-based representation of three-dimensional (3D) city models for a variety of applications, significant efforts have been made to automatically reconstruct detailed 3D building façades from terrestrial LiDAR data. Nonetheless, in real-world applications for high-quality 3D city modeling, three major problems are typically encountered: (1) very low productivity due to fully manual operation, (2) low geometric accuracy of 3D modeling resulting from the process of reducing original LiDAR data, and (3) system failure when importing huge LiDAR data to 3D drawing software. To overcome these limitations, the present study proposes a semi-automatic method entailing a plane component detection based on RANSAC segmentation, boundary tracing of the planar components, and manual drawing of details using the remaining, significantly reduced points. The proposed method was applied to point clouds of various buildings in a high-density area in Korea. In comparison with manual operation, the proposed method was proved to improve modeling productivity in the time-consumption aspect and to facilitate operators’ accurate object drawing. However, for additional automation and completeness of 3D modeling, further study is necessary. The proposed method requires a segmentation algorithm to heuristically determine parameters for the most desirable results as well as to detect curvilinear surfaces in modeling complex and curved façades.