Improving Details of Building Façades in Open LiDAR Data Using Ground Images
2019
Shenman Zhang | Pengjie Tao | Lei Wang | Yaolin Hou | Zhihua Hu
Recent open data initiatives allow free access to a vast amount of light detection and ranging (LiDAR) data in many cities. However, most open LiDAR data of cities are acquired by airborne scanning, where points on building faç:ades are sparse or even completely missing due to occlusions in the urban environment, leading to the absence of faç:ade details. This paper presents an approach for improving the LiDAR data coverage on building faç:ades by using point cloud generated from ground images. A coarse-to-fine strategy is proposed to fuse these two-point clouds of different sources with very limited overlaps. First, the faç:ade point cloud generated from ground images is leveled by adjusting the facade normal to perpendicular to the upright direction. Then leveling faç:ade point cloud is geolocated by alignment between images GPS data and their structure from motion (SfM) coordinates. Next, a modified coherent point drift algorithm with (surface) normal consistency is proposed to accurately align the faç:ade point cloud to the LiDAR data. The significance of this work resides in the use of 2D overlapping points on the building outlines instead of the limited 3D overlap between the two-point clouds. This way we can still achieve reliable and precise registration under incomplete coverage and ambiguous correspondence. Experiments show that the proposed approach can significantly improve the faç:ade details in open LiDAR data, and achieve 2 to 10 times higher registration accuracy, when compared to classic registration methods.
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