Image characterization method for shaft type and wheel disc type three-dimensional models

Abstract

The invention discloses a shaft and wheel disc type three-dimensional model-oriented graphical representation method, and belongs to the field of three-dimensional model representation and retrieval, and the method comprises the following steps: S1, scanning shaft and wheel disc type three-dimensional models, specifically, reading the three-dimensional models, initializing parameters of a radial rotation scanning algorithm, constructing an svg canvas, and scanning the three-dimensional models; performing radial step scanning by applying a line segment intersection device, solving spatial information of the three-dimensional model, storing the spatial information in an svg canvas, and making a characteristic spectrum of the mother three-dimensional model and the candidate three-dimensional model; and S2, calculating the similarity between the mother three-dimensional model and the candidate three-dimensional model, specifically, reading a characteristic spectrum, performing threshold segmentation, converting the characteristic spectrum into a characteristic matrix, calculating the similarity between the mother three-dimensional model and the candidate three-dimensional model according to the characteristic matrix, and storing a similarity result. According to the method, the three-dimensional models of the shaft type and the wheel disc type are converted into the corresponding images, and the similarity calculation is carried out, so that rapid retrieval and reuse of the three-dimensional models are realized.

Description

technical field [0001] The invention relates to the field of three-dimensional model representation and retrieval, in particular to an image representation method for shaft and roulette three-dimensional models. Background technique [0002] Retrieval of 3D models and computer-aided data reuse are hot topics in recent years. Among them, the model retrieval method based on statistics is simple in principle and fast in calculation, but it is often only suitable for the description of global features, and cannot effectively distinguish local areas in the model that are completely different in design; it is suitable for classifying different types of models. However, the discrimination of the same model is not good; the method based on abstract graph can better describe the detailed structure of the model, and can express appropriate semantic information, and realize the local matching of the model; The projection of multiple viewing angles can obtain multiple two-dimensional p...

AI Technical Summary

Problems solved by technology

[0003] (1) The calculation and storage of the method based on the abstract graph is complex, and the calculation to realize the perfect matching of two graphs is usually a non-deterministic polynomial problem, and the generality of the algorithm is poor;

[0004] (2) The method based on the projection view is too computationally intensive and slow, and can only compare the similarity based on the surface of the 3D model, and does not involve the comparison of its internal structure

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