Object Simplification on Static and Dynamic Models

• Main Authors: Pai-Feng Lee, 李白峰
• Other Authors: Bin-Shyan Jong
• Format: Others
• Language: en_US
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/28161524511612493480

博士 === 中原大學 === 電子工程研究所 === 95 === This investigation presents two novel rapid and effective point simplification algorithms for static model simplification utilizing point cloud without normals. And this thesis also presents a simplification algorithm for dynamic model, also called animation model, using principal component analysis. For static model simplification, octree based point cloud simplification and DSO feature based point cloud simplification are proposed. The octree based point cloud simplification adopted local coplanar analysis to obtain the relevant points from a point set sampled from 3D objects. The local coplanar analysis, on the basis of an oc-tree data structure with an inner point distribution of a cube, can determine whether these points are coplanar. The proposed approach successfully extracts the feature area from point set. According to the object’s surface variation and user definition, the scheme also modulates the density of these relevant points. The relevant points, called the base model, were reconstructed to triangular mesh. In addition to the successful reconstruction, the error rate of the base model within a specific tolerance level. Compared with the traditional methods in which surface reconstruction is completed prior to mesh simplification, this approach applies point cloud simplification prior to surface reconstruction to improved time expense and calculation cost. By using the octree data structure, this thesis proposes some hierarchical rendering for the recon-structed model to suit user demand and produce a uniform or feature-sensitive simpli-fied model that facilitates rapid further mesh based applications. Finally, output of the proposed method is a hierarchical triangular mesh that inherently supports generation of multi-resolution representations for the applications of level of detail. This thesis also proposes a Discrete Shape operator (DSO) feature based method for effective low-error point cloud simplification method to retain the physi-cal features of models. The DSO value is adopted to extract the features of the point cloud models, and the feature vertices are postponed to simplify. The proposed method improves the quadric error metric of the vertex pair contraction; it not only effectively simplifies the model while retaining the features of the object model but also decreases the pre-processing time cost for feature analysis. This algorithm pro-poses a method to obtain unique simplified model for each model. The unique sim-plified model obtained can significantly reduce the computation cost about 70.6% than mesh simplification which reconstruct original points first. In other words, the proposed method using DSO can adaptively collect more geometric information, par-ticularly on the high variation surfaces and the feature points. The DSO extraction can help to successfully reconstruct the simplified point cloud, preserve the features of simplified models, and reduce the errors caused by point cloud simplification. For dynamic model (animation model) simplification, this thesis investigates the use of the affine transformation matrix when employing Principal Component Analy-sis (PCA) to simplify the data of 3D animation models. Satisfactory results were achieved for the common 3D models by using PCA because it can simplify several related variables to a few independent main factors, in addition to making the anima-tion identical to the original by using linear combinations. The selection of the prin-cipal component factor (also known as the base) is still a subject for further research. Selecting a large number of bases could improve the precision of the animation and reduce distortion for a large data volume. Hence, a formula is required for base selec-tion. This thesis develops an automatic PCA selection method, which includes the se-lection of suitable bases and a PCA separately on the three axes to select the number of suitable bases for each axis. PCA is more suitable for animation models for apparent stationary movement. If the original animation model is integrated with transformation movements such as translation, rotation, and scaling, the resulting animation model will have a greater distortion in the case of the same base vector with regard to apparent stationary movement. This thesis is the first to extract the model movement characteristics using the affine transformation matrix and then to compress 3D animation using PCA. The affine transformation matrix can record the changes in the geometric transformation by using 4 × 4 matrices. The transformed model can eliminate the influences of geo-metric transformations with the animation model normalized to a limited space. Sub-sequently, by using PCA, the most suitable base vector (variance) can be selected more precisely.