Research On3D Model Retrieval And Related Methods

Digital geometry processing which takes3D model as the process object is rapidly developing. This phenomenon has made3D model retrieval become a research focus. What's more, feature extraction is a key technology in the process of the3D model retrieval and the retrieval result is directly determined by the quality of the features. Meanwhile, the diversity of input samples has brought great convenience to users.Under the support of National Natural Science Foundation Project "Sparse reconstruction method for optical molecular tomography combing a three-dimension statistical deformable model"(Granted No.61372046), based on the research status of the development on the extraction of3D model feature as well as existing3D model retrieval platforms, by synthetically utilizing differential geometry, wavelet analysis and digital image processing method and so on, this thesis researches on3D models retrieval which uses3D models and two-dimensional images as input samples.3D model features are extracted in many ways, such as local detail features, entire distance features and entire constraint feature of contour barycenter and so on. The result of the multi-feature fusion is applied to the3D model retrieval. Meanwhile, the image deformation method has been researched to improve the efficiency of the3D model retrieval based on two-dimensional images. The main work completed in the thesis is listed as follows:(1) A3D model retrieval method based on harmonic analysis of spherical function with local features is proposed. The mean curvature of3D model facet vertex is quadratic differential of function, which expresses3D model's detail information. The thesis structures a function which uses the spatial position and the mean curvature of the3D model surface feature point. The new local features of the model with the characteristic of no changing in rotation, translation and zoom is obtained by spherical harmonic analysis of the function.(2) A3D model retrieval method based on the fusion of local and entire features is proposed. The entire feature which is the distance from the surface points to the model barycenter is used to weight new local feature and a group of feature vectors is gained. The new features contain both local and entire information of the3D model. The new feature is used to retrieve models in the3D model database and the experimental results show that the use of new feature the recall ratio and precision ratio are higher than the single use of entire or local feature. (3) The entire restraint feature extraction method of the two-dimensional image contour barycenter is proposed. Aiming at input samples of the two-dimensional image or hand-drawing image, the object contour in the two-dimensional image is extracted by using combination of the improved field force optimize GVF-Snake model and ray method which improves the accuracy of object contour extraction; In order to make the3D model's two-dimensional contour in3D model feature database contain extensive information, the depth buffer projection method is adopted to extract the two-dimensional image contour of3D model; the contour barycenter distance is used to weight the contour tree descriptor, so that the relative position of the contour can be restricted. Moreover, fused with contour significant degree to improve the recognition of the contour, then the contour barycenter constraint feature descriptor is obtained. Finally, the overall constraint feature descriptor of two-dimensional image contours barycenter and feature descriptor Fourier of the two-dimensional contours to retrieve3D models is fused. The experimental results show that using the integration of spatial and frequency domain characteristics of the two-dimensional contour descriptor have higher recall ratio and precision ratio than using Fourier feature descriptor.(4) An image deformation method based on wavelet filter using moving least squares (Moving Least Squares, MLS) is proposed. According to3D model retrieval based on two-dimensional image contour, the existing two-dimensional image or hand-drawing image of users can be closer to the two-dimensional images of ideal3D model by deformation. By improving the old ways which deform the image directly, this method can filter the original image into a high and a low frequency sub-image with wavelet which can better describe the detail and contour information respectively. As contour changes affect the extraction based on two-dimensional contour feature directly, so the low frequency image is deformed by MLS and the high-frequency sub-image isn't processed which can keep the detail information effectively. The final deformation image will appear by adding the deformed contour information to the details. The experimental results show that this method can be operated easily and it is easy for user to express ideal image, therefore the deformation results are satisfactory and realistic.(5) An image deformation method based on wavelet filter using control curves and moving least squares is proposed. As the control curve has better control ability as well as better capacity in smoothing image deformation than control point, so the image deformation based on control points is improved. After wavelet filtering, according to contour information of original image or setting the key points in the needed deformation place, control curves can be generated and moved to target position to realize the deforming of image by using moving least squares. At last, the final deformation image can be obtained by adding the detail information to the contour information after deformation. This method can improve the success rate of the deformation by well describing the contour information. Because of having filtered out a great quantity of unnecessary deformation points, this method not only can improve the deformation peed greatly, but also can be applied to image deformation in general field.