Color-Based Algorithm for Automatic Merging of Multiview 3D Point Clouds

Abstract

In this article, a method of merging point clouds using the modified Harris corner detection algorithm for extracting interest points of textured 3D point clouds is proposed. A new descriptor characterizing point features for identifying corresponding points in datasets is presented. The merging process is based on the Random Sample Consensus (RANSAC) algorithm, which enables calculation of the geometric transformation between point clouds based on a set of interest points that includes incorrect samples, called outliers. The proposed processing path is designed to integrate many directional measurements, which are acquired with a 3D scanner and are represented as unsorted point clouds (x, y, z) with color information (R, G, B). Exemplary measurements shown in this article represent sections of ceiling in the King's Chinese Cabinet of the Museum of King Jan III's Palace at Wilanow in Warsaw, Poland, as well as some more complex objects. Experimental verification confirms the effectiveness of the proposed method in integrating directional measurements of objects with detailed texture, particularly if they have no unique geometric features.