Three-dimensional model interactive browsing method based on separation degree

Abstract

The invention provides a three-dimensional model interactive browsing method based on separation degree. A user can preview and browse models in a thinking evolution mode. The three-dimensional model interactive browsing method comprises the following steps of S1, inputting a group of heterogeneous three-dimensional models different in shapes; S2, screening a series of quad trees of a rootless classification tree for organizing all models through multiple model comparison operator measurement; S3, using the quad trees screened in the step S2 as inputs, establishing a rootless classification tree and organizing all models by different levels according to categories; S4, conducting switched browse on all models based on the separation degree of each model relative to a selected model according to the classification tree.

Description

technical field [0001] The invention relates to the technical field of three-dimensional model processing, in particular to an interactive browsing method for three-dimensional models based on degree of separation. Background technique [0002] With the popularity of modeling tools and the maturity of laser scanning technology, it is becoming easier to obtain and make 3D models. With the development of the Internet, more and more 3D models are produced and stored on the Internet, forming a 3D model library. The rapid increase in the number of models in model repositories has prompted novel methods to organize unordered models to meet the needs of model repositories for retrieval, preview, and browsing. [0003] Any organizational approach must be based on mechanisms for similarity comparisons between individual models. In recent years, to measure the similarity between models, researchers have proposed a series of distance measures (e.g., Chen et al. LFD [2003], Shilane et...

AI Technical Summary

Problems solved by technology

Due to the unreliability of distance operators in measuring long-distance models and the susceptibility of clustering algorithms to interference, these methods of organizing models through clustering algorithms have poor efficiency and accuracy

In particular, organizing large-scale models requires a hierarchical structure, but using some kind of hierarchical or clustering structure implies a relatively distant model, so using these hierarchical or clustering structures to organize models is not reliable

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