Point Based Simplification Algorithm using Discrete Shape Operator

• Main Authors: Ming-Tseng Lin, 林明宗
• Other Authors: Bin-Shyan Jong
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/66082010713179987081

碩士 === 中原大學 === 資訊工程研究所 === 95 === This study proposes an effective low-error point cloud simplification method to retain the physical features of models. A Discrete Shape Operator (DSO) is adopted to extract the features of the point cloud models, and the feature vertices are postponed to simplify. The proposed method improves 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 study also proposes a method to obtain unique simplified model for each model and the time cost involved in calculating DSO is about 17.84% of the execution time. To compare the error with simplified model and original model, the Tight Cocone reconstruction algorithm proposed by Dey is applied, and the Metro tool is used to measure some error factors. The unique simplified model obtained by this study can significantly reduce the computation cost about 71.7% than mesh simplification which reconstruct original points first. In other words, the proposed method using DSO can adaptively collect more geometric information, particularly on the highvariation surfaces and the feature points.