A Robust Automatic Method to Extract Building Facade Maps from 3D Point Cloud Data

作者：Bing Yu, Jinlong Hu, Xiujun Dong, Keren Dai, Dongsheng Xiao, Bo Zhang, Tao Wu, Yunliang Hu, Bing Wang · 发表于：Remote Sensing · 年份：2022 · DOI：10.3390/rs14163848 · 被引用次数：8 · 研究领域：Remote Sensing and LiDAR Applications、3D Surveying and Cultural Heritage、Satellite Image Processing and Photogrammetry

Extracting facade maps from 3D point clouds is a fast and economical way to describe a building’s surface structure. Existing methods lack efficiency, robustness, and accuracy, and depend on many additional features such as point cloud reflectivity and color. This paper proposes a robust and automatic method to extract building facade maps. First, an improved 3D Hough transform is proposed by adding shift vote and 3D convolution of the accumulator to improve computational efficiency and reduce peak fuzziness and dependence on the step selection. These modifications make the extraction of potential planes fast and accurate. Second, the coplane and vertical plane constraints are introduced to eliminate pseudoplanes and nonbuilding facades. Then, we propose a strategy to refine the potential facade and to achieve the accurate calibration and division of the adjacent facade boundaries by clustering the refined point clouds of the facade. This process solves the problem where adjoining surfaces are merged into the same surface in the traditional method. Finally, the extracted facade point clouds are converted into feature images. Doors, windows, and building edges are accurately extracted via deep learning and digital image processing techniques, which combine to achieve accurate extraction of building facades. The proposed method was tested on the MLS and TLS point cloud datasets, which were collected from different cities with different building styles. Experimental results conf...