An efficient development of 3D surface registration by Point Cloud Library (PCL)

This paper presents how to utilize the open source Point Cloud library (PCL) to develop a series of computational registration processes efficiently and robustly. The registration is the key process of the Digital Face-Inspection (DFI) system. In 2009, the DFI system was developed to assist dentists to detect deviations of patients' face shapes before/after an orthodontic treatment. The system combined the technologies of 3D scanning and Reverse Engineering together to achieve the goal of creating a better quality environment. Regarding the visual analysis made by the DFI system, dentists can adjust their treatments to guarantee that their treatments are on the right track. This is definitely very helpful to create a high-quality dental environment. The inputs of the DFI system are a set of point clouds generated by 3D scanning systems. The data type of point cloud present an object by a huge amount of surface points. A completed DFI process requires two scanning independent scanning routines and those scanning routines generated point cloud by their reference coordinate systems. This event causes that generated point clouds can't be applied into the deviation analysis directly. Before the analysis, the DFI system introduced the Iterative Closest Point (ICP) algorithm to align two point clouds as close as possible. This is to force two point clouds in a same coordinate system. However, the ICP algorithm is a local operation process. Therefore, a coarse registration is required for finding an optimal initial alignment. The registration includes a lot of computational algorithms and makes the DFI development very complicated. Fortunately, an open source called Point Cloud Library is available for helping us to develop this registration easily and efficiently. PCL collected hundreds of functions and algorithms for handling point cloud data in various applications. This paper demonstrates how to introduce PCL to build up the DFI system. In addition, we also presented how to utilize PCL to improve the efficiency of the DFI system.