Abstract
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 façades are sparse or even completely missing due to occlusions in the urban environment, leading to the absence of façade details. This paper presents an approach for improving the LiDAR data coverage on building façades 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 façade point cloud generated from ground images is leveled by adjusting the facade normal to perpendicular to the upright direction. Then leveling façade 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 façade 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 façade details in open LiDAR data, and achieve 2 to 10 times higher registration accuracy, when compared to classic registration methods.
Highlights
In recent years, there has been a significant push from the open data initiatives in many NorthAmerican cities [1,2,3] or the large projects such as Infrastructure for Spatial Information in the European Community (INSPIRE) [4,5] proposed by European Commissions to provide vast amounts of open datasets that include open LiDAR data [6,7]
This paper presents a novel method for improving open LiDAR data on the building façade using the façade point cloud generated from ground images
Not as good as the result of target centres registration (TCR) methods, the proposed method achieves the best accuracy compared to Iterative Closest Point (ICP) and Normal-Distributions Transform (NDT) methods due to the use of similarity on 2D outlines of buildings
Summary
There has been a significant push from the open data initiatives in many NorthAmerican cities [1,2,3] or the large projects such as Infrastructure for Spatial Information in the European Community (INSPIRE) [4,5] proposed by European Commissions to provide vast amounts of open datasets that include open LiDAR data [6,7]. Due to the free access to these open LiDAR data, new avenues of research for students, researchers, and other LiDAR data user community have been opened [8,9,10] These open LiDAR data are often sparse and incomplete, or even entirely void on the façades due to the viewpoint and occlusions in the urban environment. This problem makes it difficult to achieve fine building reconstruction with high levels of detail (LoD) [11]. Ground imagery capture devices such as off-the-shelf digital cameras, smartphones with GPS and digital compass have become ever prevalent They allow us to acquire a number of high-resolution images of the building façade through crowd-sourcing at low cost. Considering that ground images are complementary to open LiDAR data doi:10.20944/preprints201810.0354.v1
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