Outliers Detection Method Based on Dynamic Standard Deviation Threshold Using Neighborhood Density Constraints for Three Dimensional Point Cloud

In 3D point clouds reverse engineering, the outliers detection plays a key role on the subsequent processing. However, when the point cloud has big change density distribution, the detection of outliers becomes very difficult. In order to get a feasible detection result, improve the detection ability and adaptivity, an outliers detection method was proposed, which based on dynamic standard deviation threshold using k-neighborhood density constraints. This method fully considered the density difference of the obtained point cloud, and intro-duced the density characteristics into calculation of the determining threshold. Firstly, the target point cloud by pass-through filtering was extracted, and the invalid points were removed. Then the detection principle was ana-lyzed, the k-neighborhood density estimation method was presented. Finally the dynamic standard deviation threshold constrained by the k-neighborhood density was calculated, the different constraints for outer regions and inlier regions were obtained, and a better detection result for point cloud with big change density distribution was got. Experimental results show that the method can apply to the highly variable density distribution point cloud, get a feasible detection result, improve detection effect and performance, and is positive to practical appli-cations. © 2018, Beijing China Science Journal Publishing Co. Ltd. All right reserved.