An Efficient Global Constraint Approach for Robust Contour Feature Points Extraction of Point Cloud
Xijiang Chen, Bufan Zhao
Abstract
The contour feature points of object point cloud are the main features of human perception on target, and play an important role in many fields such as indoor model reconstruction, object detection and location. In this paper, 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 two-dimentional view. (2) According to the direction of main feature vector, a two-dimensional 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 two-dimensional 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 anti-noise performance of the proposed method is 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.