Three-dimensional model retrieval system and method based on parts division of hand-drawn draft

Abstract

The present invention discloses a three-dimensional model retrieval system and method based on parts division of a hand-drawn draft. The system comprises: a preprocessing module, a part marking module, a sampling point feature extracting module, a parts division module, and a similarity calculation and total score sequencing module, wherein the preprocessing module is used for denoising a hand-drawn query draft to obtain a grayscale image, and performing binarization processing, boundary extension processing and image hole filling processing on the grayscale image to obtain a processed image; the part marking module is used for performing equal-interval sampling on the processed image, and adding part tags at sampling points; the sampling point feature extracting module is used for extracting various feature vectors of the sampling points; the parts division module is used for performing division model training according to the various feature vectors of the sample points to which the part tags have been added; and the similarity calculation and total score sequencing module is used for performing local similarity calculation on parts, performing sequencing according to total scores, and feeding back a sequencing result to a client. Implementation of the embodiments of the present invention can make three-dimensional model retrieval based on the hand-drawn draft more accurate and effective.

Description

technical field [0001] The invention relates to the technical field of computer image processing, in particular to a three-dimensional model retrieval system and method based on hand-drawn sketch component segmentation. Background technique [0002] In recent years, with the rapid development of computer-aided design, computer-aided manufacturing, virtual reality, 3D animation and 3D games, the number of 3D models on the Internet has increased dramatically. However, the 3D model is different from traditional multimedia information such as pictures, audio or video, and it contains many detailed information that is difficult to express in words. [0003] However, current 3D model retrieval methods are still unsatisfactory in application. On the one hand, when users need some kind of 3D model resources, they often do not have ready-made model files at hand; on the other hand, with the rapid popularization of touch screens and electronic pens, users can easily outline the model...

AI Technical Summary

Problems solved by technology

[0011] The traditional method based on text keywords cannot be well applied in the scene of 3D model retrieval. There are three main reasons for this: First, 3D models have complex topological structures and shape features, and there are many kinds of them. Detailed information is difficult to express clearly with a few keywords

Second, the tagging process of adding text keywords to the 3D model needs to be done manually, and the number of 3D models on the Internet is growing rapidly, and the manual adding method is cumbersome and the workload is also heavy

Third, because different people have different understandings of various 3D models, the keywords they think of to describe them are also quite different, which may easily lead to inconsistencies between the search keywords and the labels of the target model, and the way of manually adding keyword labels is affected Limited to the types of label languages, it is not convenient for international promotion

[0013] For model retrieval based on 3D model instances, the disadvantage is that it is usually difficult for the user to find a very suitable model instance as input when the user initiates the retrieval, because if the user has a very suitable target model at hand, then there is no need for retrieval up

[0014] For 3D model retrieval based on hand-drawn sketches, its disadvantage is that it usually does not consider the overall structure of the sketch, it is only considered based on the local area; another disadvantage is that it is sensitive to the style of the user's sketch, if the user is in the local outline If the line drawing style is quite different, the difference in the extracted results will be enlarged, which will inevitably affect the final retrieval results

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