The 3D Point cloud derived especially from drone-derived images is highly unstructured, redundant and has varied density. These point clouds need to be segmented and classified into different groups representing similar characteristics in the scene presented which is a challenging task especially when the 3D scene contains a mix of varied man-made or unstructured natural scenes such as vegetation etc. Successful operation of such technology will lead to a wide variety of remote sensing, computer vision and robotics applications. In this paper, we have used a hybrid approach for effective segmentation of the point cloud. The combination of RANSAC, DBSCAN and Euclidean method of Cluster Extraction proved to be useful for precise segmentation and classification of the point cloud.
The Segmentation of Drone Image derived 3D Point Cloud Using a Combination of RANSAC, DBSCAN and Clustering Methods
Lecture Notes in Civil Engineering
International Conference on Unmanned Aerial System in Geomatics ; 2021 ; Roorkee, India April 02, 2021 - April 04, 2021
2023-03-16
12 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Unsupervised Change Detection Using RANSAC
British Library Conference Proceedings | 2006
|