Realistic correction of sky-coloured points in Mobile Laser Scanning point clouds. (May 2022)

• Record Type: Journal Article
• Authors: González, Elena; Balado, Jesús; Arias, Pedro; Lorenzo, Henrique
• Is Part Of: Optics & laser technology. Volume 149(2022)
• Publication Date: 2022-05
• Subjects: LiDAR -- Point cloud processing -- Image processing -- Coloured point cloud -- Mobile Mapping Systems -- Lab colour space; Optics -- Periodicals; Lasers -- Periodicals; Electronic journals; 621.366
• DOI: 10.1016/j.optlastec.2021.107807
• Languages: English
• ISSNs: 0030-3992

Abstract

Highlights: Point cloud processing based on Lab space colour. Detection and correction of sky-coloured points caused by calibration errors. Detection of sky-coloured points in eight real case studies with an average 94.7% F1-score. Deviation from true colour of 0.976 ± 1.280 intensity levels tested on point clouds with synthetic errors. Abstract: The enrichment of the point clouds with colour images improves the visualisation of the data as well as the segmentation and recognition processes. Coloured point clouds are becoming increasingly common, however, the colour they display is not always as expected. Errors in the colouring of point clouds acquired with Mobile Laser Scanning are due to perspective in the camera image, different resolution or poor calibration between the LiDAR sensor and the image sensor. The consequences of these errors are noticeable in elements captured in images, but not in point clouds, such as the sky. This paper focuses on the correction of the sky-coloured points, without resorting to the images that were initially used to colour the whole point cloud. The proposed method consists of three stages. First the region of interest where the erroneously coloured points are accumulated, is selected. Second, the sky-coloured points are detected by calculating the colour distance in the Lab colour space to a sample of the sky-colour. And third, the colour of the sky-coloured detected points is restored from the colour of the nearby points. The method is tested in ten real case studies with their corresponding point clouds from urban and rural areas. In two case studies, sky-coloured points were assigned manually and the remaining eight case studies, the sky-coloured points are derived from the acquisition errors. The algorithm for sky-coloured points detection obtained an average F1-score of 94.7%. The results show a correct reassignment of colour, texture, and patterns, while improving the point cloud visualisation.