Point Cloud Segmentation Based on Moving Probability
Shou Bin Liu, Kun Feng · Applied Mechanics and Materials · 2013
This paper presents a novel automatic algorithm for point cloud segmentation by using moving probability. An arbitrary point in point cloud is selected as the first seed point. Starting from the seed point, moving probability between the starting point and each of neighborhood points is estimated. Once one or more points with probabilities greater than a given threshold are identified, the starting point will move to these neighborhood points and new starting points are generated. Moving probabilities are estimated again and starting points move continually until all calculated probabilities are less than the threshold. Visited points are segmented from point cloud data. The second seed point is selected arbitrarily from the rest of points and the process is repeated. As a result, point cloud is segmented into individual feature regions. Experimental results show the effectiveness of the proposed algorithm.