Abstract
Reliable feature extraction from 3D point cloud data is an important problem in many application domains, such as reverse engineering, object recognition, industrial inspection, and autonomous navigation. In this paper, a novel method is proposed for extracting the geometric features from 3D point cloud data based on discrete curves. We extract the discrete curves from 3D point cloud data and research the behaviors of chord lengths, angle variations, and principal curvatures at the geometric features in the discrete curves. Then, the corresponding similarity indicators are defined. Based on the similarity indicators, the geometric features can be extracted from the discrete curves, which are also the geometric features of 3D point cloud data. The threshold values of the similarity indicators are taken from[0,1], which characterize the relative relationship and make the threshold setting easier and more reasonable. The experimental results demonstrate that the proposed method is efficient and reliable.
Highlights
Rapid developments in 3D rangefinder technology allow us to accurately and conveniently digitize the shape and surface of physical objects [1, 2]
A novel method is proposed for extracting the geometric features from 3D point cloud data based on discrete curves
A new method is proposed to extract the geometric features from the structured 3D point cloud data, which are generated by regular sampling on a certain grid, based on discrete curves in this paper
Summary
Rapid developments in 3D rangefinder technology allow us to accurately and conveniently digitize the shape and surface of physical objects [1, 2]. In the most above-mentioned methods, the thresholds of geometric indicators (such as normal vectors and principal curvatures) or error indicators (e.g., the average errors obtained on plane and surface fitting) need to be set for the whole point cloud region to identify the discontinuities and extract the geometric features. Unknown dimensions and noise distribution, and several iterations may be needed until the proper values are found [11] This is because these indicators have no explicit ranges for the whole point cloud region of an unfamiliar object. To avoid this problem, a new method is proposed to extract the geometric features from the structured 3D point cloud data, which are generated by regular sampling on a certain grid, based on discrete curves in this paper. The threshold value of the similarity indicator is taken from [0, 1], which characterizes the relative relationship and makes the threshold setting easier and more reasonable
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