Study On Data Processing Technology Of 3D Cloud Points

In computer-aided geometric design, computer animation, reverse engineering, medical diagnosis, entertainment and other applications, the processing techniques of 3D point cloud data become more and more attentive. People can use different ways to get three-dimensional computer data of real-world objects. The content of this research is the use of 3D laser scanner to obtain the discrete 3D point data cloud and by smooth-ing, de-noising, feature detection, simplification to get more accurate and suitable data model for representation of real world objects. Then on this basis, the parameterization and surface reconstruction for 3D point cloud model are completed to obtain the para-meters, surfaces and polygonal model description of objects. Finally we output the re-sult to virtual environment and complete computation from real world objects into the virtual world reality.Main contributions of this thesis can be summarized as follows:1. Two 3D point cloud data de-noising algorithms are proposed. The first is point cloud weighed fuzzy c-means clustering (PWFCM) and point cloud bilateral filter me-thod(PBF) algorithm. We define the noise as large-scale noise and partly smooth small-scale noise. Large-scale noise is deleted directly and the small-scale noise will be moved to near the clustering center, then the remainder small-scale noise is smoothing by point cloud bilateral filter method (PBF). The second algorithm is for more complex cases. the data are de-noised first by point boundary detection (PBD) method, which detect the boundary point as noise and delete it, then delete the large-scale noise by point cloud weighed fuzzy c-means clustering (PWFCM). The algorithms proposed can decrease the amount of data and avoid over-smoothing.2. We propose a curvature and density based feature point detection method (CDFD). A new feature parameter is defined which considers the average distance and the normal angle between the point and its neighboring points and point curvature pa-rameter. This parameter shows local geometry information. We define also a feature threshold from data density and maximum distance of data points. Then the feature points could be recognized when its density parameter is bigger than the threshold. Ex-perimental results show that the new approach can detect the feature points accurately for different 3D scattered point data cloud models and it can provide the good model for further simplification and surface reconstruction.3. A uniformly sampling method for 3D cloud data is proposed. After the separa-tion of feature and non-feature data, we project the non-feature data of model to a sphere by using Isocube sphere and uniform sampling. With different sampling rate, we can get multi-resolution and uniform simplification model. All feature points are kept the same, so the sharp information of 3D point cloud may be retained well.4. We propose a geometry image parameterization algorithm for 3D point cloud data. Firstly, we do spherical parameterization, octahedron parameterization and unfold the octahedron to 2D plane parameterization and get a geometry image of 3D data. This operation does not need to do trianglization and surface fitting. So it is fast and easy to compete. From the operation, we transformed the 3D model to the 2D model. Then the re-sampling and morphing of the geometry image may be applied, and inverse mapping back to 3D point cloud model.5. On the basis of feature point detection and Isocube simplification of 3D cloud data, a multi-resolution surface reconstruction method is proposed, which uses the sin-gle scale and multi scale Compactly Supported Radial Basis Function interpolation. We use conjugate gradient algorithm for solving linear equations to reduce the compu-tational load and improve computing speed. The surface reconstruction result is good.6. An experiment system for 3D point cloud data processing is established. The system may reconstruct realistic parametric description of 3D objects for virtual envi-ronment usages. The platform can fulfill different 3D point cloud data processing tasks through effective, organic integration of hierarchical and relatively independent func-tional modules of data processing, which lays a solid foundation for the further in-depth study in 3D point cloud data processing research.