Abstract: For the point cloud model with complex profiles and many holes, the existing reverse software and repair algorithm exhibit such problems as lower efficiency and excessive human-computer interaction in hole-by-hole repairing, as well as low repair accuracy and loss of features in multi-hole repairing. Therefore, it is necessary to develop an efficient and high-precision automatic repair mode aiming for feature preserving based on identification and classification of holes in point clouds. Based on the above ideas, a hole recognition-based method was proposed for separating cylindrical feature holes from general types of holes. First, boundary points of holes were extracted and collected following the criterion of maximum angle recognition; the total number of holes was calculated with Euclidean clustering. After that, cylindrical feature holes in the model were extracted using RANSAC and the set distance threshold. According to experimental results, the method can both extract from the model multiple cylindrical feature holes of different diameters, and make estimate of geometric parameters of the cylindrical surface more accurately. In this way, the automatic repairing-oriented identification of a specific type of feature holes can be achieved. 

Key words:  , point cloud model, hole identification, automatic repair, clustering algorithm, RANSAC algorithm