Automatic feature identification in 3-D measuring data

The automatic feature identification in 3-D measuring data is of great interest in many application fields e.g. metrology, computer vision or reverse engineering. In this paper we present a software tool for the fully automatic object detection and parameter estimation in unordered incomplete and even noisy point clouds with a large number of points. The software consists of three interactive modules each for model selection, point segmentation and model fitting, in which our newly developed algorithms for orthogonal distance fitting (ODF) play an important role. The ODF algorithms estimate the model parameters by minimizing the square sum of the shortest distances between the model featrue and the measurement points. The local quadric surface fitted through ODF to a randomly touched small initial patch and the point cloud provides the necessary initial information for the overall procedures of model selection, point segmentation and model fitting. The performance of the presented software tool will be demonstrated on different point clouds.