A Research On Content-based3D Model Retrieval

With the development of computer vision and computer graphics, people do notsatisfied with the visual senses in2D world. More and more people are abstract bythe3D model, because of the model acquisition, storage technology, rendering andtransforming easily in the network. It brings a3D model of the big bang era.Three-dimensional model has penetrated into all walks of life, mostly industrialproduct design relies on a three-dimensional model of the blueprint and then to thekind of production; Medical imaging equipment is also dependent three-dimensionalmodeling to provide more diagnostic criteria for doctors. Only to developthree-dimensional technology greatly can meet needs of over such a wide range.Three-dimensional model retrieval consists of two sub-processes: featureextraction and similarity calculation. Among them, the main challenge is to featureextraction, namely choose what kind of features to describe the contents of athree-dimensional model. Non-rigid three-dimensional model appears to increasethe retrieval difficult, because non-rigid model has extensive variability because itcan withstand large bending strength and broad transformation category. In thispaper we focus on feature extraction of non-rigid model. On one hand for the defectof heat kernel signature in description of three-dimensional models, we put forwardthe improvement way. On the other hand, puts forward a new retrieval method basedon feature saliency.In the feature extraction algorithm for non-rigid model, using theLaplace-Beltrami operators on three-dimensional model to describe has a wide rangof applications. We first found that heat kernel did not invariant to scale changes. Inthis paper we use Fourier transform to eliminate this factor, after using our method,it keep consistent to scale changing. In addition, this paper also improves the heatkernel by using weighted-based vertex influence method. When the connection patharound the vertex is sufficiently complex, the vertex will be more robustness totransformation, and will not bring a big impact on the overall structure of thesurrounding vertex. These vertices are defined as influence vertices. Theweighted-based influence method improved the experimental result.Further research found that regardless of heat kernel or improved ones justconcerns the macro geometry structure, the model description is not comprehensiveenough. Therefore, this paper presents a retrieval method based on saliency features.This method uses wave kernel signatures to extract micro geometric structure, andmake up for the lost information. During the comparison, we considered theimportance of the different features. We quantify the weight values, by compare the correlation between different features, and build the significant feature vector.Experimental results show that the method not only has higher retrieval accuracy,but also for a variety of transformation has strong robustness.