Fast and effective integration of multiple overlapping range images

Abstract

We present a simple and fast integration method, which can fuse registered multiple, overlapping range images effectively while preserving the detailed features. First, normal vectors of the nodes in each range image are estimated and the registered multiple overlapping range images are combined into one point set. Due to registration errors, the normal vectors of homogeneous nodes in the overlapping areas would differ from each other. Thus, the normal vectors of the nodes are corrected iteratively by weighted averaging their neighboring normal vectors. Second, the combined point set is partitioned into a number of small clusters using the k-means clustering algorithm based on user-defined space interval. The clusters subsequently are subdivided into smaller subclusters by using the standard normal vector deviation as a measure of cluster scatter. Finally, in the cluster, the local maxima model, obtained using the mean shift clustering algorithm, is employed to represent the cluster. The proposed method is fast because it does not need to detect iteratively the overlapping areas, which is usually time consuming. In addition, because of the noisy filtering property of the mean shift clustering algorithm, the novel method can produce a smooth and watertight point surface while preserving the detailed features.