Research Of Point-based Techniques On Unorganized Point Cloud

Advances in 3D scanning technologies have promoted the emergence and rapiddevelopment of point-based techniques. Point-based techniques, by which points are used assurface modeling and rendering primitives, has become an important research field of reverseengineering. The particular dominance of point-based techniques includes the efficiency atreconstructing and rendering very complex objects and environments, capability of dealingwith dense scattered point cloud, and simplicity of rendering algorithms. Based on theoverview of point-based techniques, several key issues including data reduction, feature lineextraction, surface modeling and editing are studied in this dissertation, which is sponsored bythe National Key Research Project of the 10th five-year-plan of China (Grant No.2001BA201A02). Data reduction is the first preprocessing step of point-based data treatment. To avoid lossof engineering information hidden in the measured object, a data reduction algorithm on thebasis of fuzzy clustering analysis is proposed. By introducing geometric similaritymembership, surface variation can be naturally represented, forcing samples to gather inregions where surface varies drastically. And imperative constraint similarity membership isintroduced to reflect engineering demand of designers, which is in favor of retainingengineering detail features. Feature line extraction from a point cloud is another necessary preprocessing step forsurface reconstruction. To satisfy the demand for stability and accuracy of feature lineextraction, a multi-scale feature line extraction algorithm based on digital image thinning ispresented. Local entropy and repeatability rate are introduced to classify points according tothe likelihood that they belong to some feature at different size of local window, whichachieves robust and stable feature point detection for noisy surfaces. By mapping theextracted feature point cloud into 2D digital images and thinning the images, smooth featurelines are recovered. Scanty data can be dealt with and the top-quality feature lines can berecovered. Surface reconstruction is the key problem of point-based data treatment. To save time onsurface reconstruction and avoid gaps of the reconstructed surface when parts of the data getlost during transmission, a new surface reconstruction algorithm by use of decomposition of IIsurface elements is discussed. Similar to the theory of Advancing Front Method, theconsistent tangent plane estimation of each sample is performed by constructing local loopsand advancing the center loop. The volume enclosed by the given point cloud is approximatedwith the set of overlapping surface elements built at each sample point. Surface editing is indispensable in point-based data treatment. To achieve a flexiblecapability for surface editing, a superquadric-based general constrained deformationalgorithm is designed. By building a superquadric-based general constrained deformationmodel, surface can be deformed according to user-specified curvilinear displacement underconstraints, which can consist of points, lines, surfaces and volumes. During surface editing,new samples are inserted to the original point cloud and located in proper positions by usingweighted local entropy method to preserve the overall sampling density. On the basis of the above theoretic achievement, a point-based data treating system from3D unorganized data points is developed and embedded in 3D CAD system - Intesolid.