| Summary: | 碩士 === 國立成功大學 === 資訊工程學系碩博士班 === 90 === Due to the fast development of three dimensional model in many fileds, like game, animation, network, mechanical engineering and entertainments, we believe that searching three dimensional models in database will become more important.
A traditional searching method, keyword searching, is not a stable method. Because the classify standard of each people is different, it can’t satisfy our requirement in three dimensional retrieval. In two dimensional image retrieval, the problem is easier because only one information, color, in a image. But in three dimensional model retrieval problem, there isn’t a standard function which can evaluate similarity between two models. We use a matching method to evaluate similarity between two skeletal graphs of models, namely 2-pass axis-to-axis matching method. The method is like a jigsaw puzzle.
We first use geodesic distance to establish a skeletal graph - Reeb graph. It can roughly represent the skeleton of model and invariant to translation, rotation, scaling. Then we match two Reeb graphs and evaluate similarity. When matching two Reeb graphs, a concept of matching significant node first is used. It is different to depth-first and width-first, and we will matching most significant node first.
We don’t need to subdivide triangles of model by using modified fast marching method, and we can approximate the geodesic distance. Building Reeb graph by geodesic distance and evaluating similarity between two Reeb graph by using our 2-pass axis-to-axis matching method.
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