Vol. 2 No. 1
Articles

The Feature Extraction from Point Clouds using Geometric Features and RANSAC Algorithm

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  • Ramazan Alper Kuçak
Ramazan Alper Kuçak
Niğde Ömer Halisdemir University, Faculty of Engineering, Department of Geomatics, Turkey

Published 2022-03-30

How to Cite

Kuçak, R. A. (2022). The Feature Extraction from Point Clouds using Geometric Features and RANSAC Algorithm. Advanced LiDAR, 2(1), 15–20. Retrieved from https://publish.mersin.edu.tr/index.php/lidar/article/view/270

Abstract

The feature extraction of point clouds is essential for geomatics engineering as well as other engineering and architectural applications. Furthermore, with the recent entrance of digital twins, virtual reality, 3D city modeling, reverse engineering, and metaverse into human existence, 3D models, which are currently used in numerous technical sectors, have become increasingly important. As a result, the 3D model generating methods become more important. One of the most prevalent methodologies used by scientists is range-based modeling (e.g., laser scanning). Additionally, before being visualized or analyzed for 3D surfaces, 3D model acquisition (Light Detection and Ranging (LiDAR) or structure-from-motion (SfM)) and 2D imaging approaches are commonly converted into models such as 3D mesh and parameter surface. This study analyzed 3D point cloud data obtained with terrestrial laser scanners. Also, Many approaches to model extraction have been tried to obtain 3D models, planes, corner points, and lines by using various 3D surface analyses and  Random Sample Consensus (RANSAC) Algorithm.

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