A parallel point cloud clustering algorithm for subset segmentation and outlier detection

We present a fast point cloud clustering technique which is suitable for outlier detection, object segmentation and region labeling for large multi-dimensional data sets. The basis is a minimal data structure similar to a kd-tree which enables us to detect connected subsets very fast. The proposed algorithms utilizing this tree structure are parallelizable which further increases the computation speed for very large data sets. The procedures given are a vital part of the data preprocessing. They improve the input data properties for a more reliable computation of surface measures, polygonal meshes and other visualization techniques. In order to show the effectiveness of our techniques we evaluate sets of point clouds from different 3D scanning devices.