An Efficient Global Constraint Approach for Robust Contour Feature Points Extraction of Point Cloud

The contour feature points of object point clouds are the main features of human perception on target and play an important role in many fields, such as indoor model reconstruction, and object detection and location. In this article, we present a new method to extract the contour feature points of point cloud, which mainly includes two main contents: 1) the conspicuous and inconspicuous boundary points are extracted according to the characteristics of distribution of the azimuth between adjacent vectors in 2-D view and 2) according to the direction of main feature vector, a 2-D projection plane of adjacent points in the bounding sphere is constructed, and the crease points are extracted according to the constraint parameters model of distribution mechanism of adjacent points in the 2-D view. We evaluate the performance of the proposed method using objects of different sizes in real-world scenarios. Simultaneously, the extraction effect of contour feature points is compared with other methods, and the results show that the extraction and antinoise performance of the proposed method are superior to the other methods. Simultaneously, it is suitable not only for regular flat-shaped buildings but also for objects with irregular curvilinear architecture. Moreover, the proposed method involves only one parameter that needs to be tuned, and the parameter can be quickly obtained based on the distance resolution.

Automated summary

This article presents a new method for extracting contour feature points from point clouds, which includes extracting conspicuous and inconspicuous boundary points based on the distribution characteristics of azimuth between adjacent vectors in 2D view, and constructing a 2D projection plane and extracting crease points based on the distribution mechanism of adjacent points in the 2D view. The proposed method shows superior extraction and anti-noise performance compared to other methods.