Point Cloud Processing In Reverse Engineering

Since the 90s of 20th century, due to the violent market competition, higher and higher requirements for the time of product research and development and the speed of product updating are emerging. Brand new technologies on design and manufacture to satisfy market demands come out. Reverse Engineering (RE) is one of those wide-used technology to short the product research and development time. The definition of RE is that given a three dimensional real model, constructing a integrated Computer Aided Designed model which can be modified by three dimensional modeling tool like Solidworks, from the discrete space coordinate data of the real model. The digital measure equipment can used to acquire the discrete data also called point cloud. Therefore Reverse Engineering can divide into three processes: data acquisition, data processing, and abstract & model. In this paper we mainly discuss the later two processes: how to efficiently process, display and render the model data (data processing content) and the extraction of feature curve and the fitting of feature surface (abstract & model).With the development of digital measure equipment, the improvement of automatic acquisition and precision, the quantity of measure data from the model grows rapidly. General laser measure equipment now can easily acquire ten thousands even hundred thousands data. The data is so large that it not only burdens the system, but also reduces the efficiency of the later process. So it is very important to pre-process the origin data before the abstract & model phase. The pre-processing phase includes point cloud sampling, smoothing and filtering, segmentation and merging, coordinate transform, deriving and rearrangement, data sorting etc. During the introduction of these algorithms and research development, innovative or improved algorithm is put forward on some points. Especially the point cloud sampling, a new algorithm of preservation of boundary points while the large-scale compression is proposed and implemented. This paper will discuss the pre-processing later in detail.The next phase of pre-processing is abstract & model. In this paper, the modeling of point cloud includes the extraction of feature curve especially the boundary curve and fitting surface from scattered data especially the quadric surface. There are two simple ways in the extraction of feature curve. One is delaunay the point cloud to a mesh, and then detection the feature curve according to the topology relation of the mesh. Another way is extracting the feature curve directly from the point cloud by fitting and sorting the feature points will be discussed later in detail. Similarly, the extraction of feature surface has many ways. Surface can loft or apply the Bound UV Curve Network from the feature curve. Another way is independent on the curve. It constructs the feature surface by fitting the point cloud. The first method aims to free surface construction, but depends on the curve's direction and will lost the original model shape if the curve direction which representing thesurface direction is incorrect. On the other hand, the later can make surface with high precision. Quadric surface fitting usually adopts the second way and will be discussed later.This paper aims to the research of point cloud processing in reverse engineering, including the pre-processing of point cloud and abstract model like the extraction of feature curve and the fitting of surface.The main contribution of this paper is that, creative idea is proposed especially in point cloud sampling, the extraction of boundary feature curve and quadric surface fitting based on analyzing and summarizing the current algorithm of the pre-processing. At last, some suggestions about future research are proposed.