A 3D point cloud recognition method based on B-spline surface similarity detection

Abstract

The invention relates to a 3D point cloud recognition method based on B-spline surface similarity detection, which is characterized in that the method comprises the following steps of1 modeling and fitting the three-dimensional point cloud by using B-spline surface equation; 2 sampling that fitting B-spline surface by adopt different parameter sampling methods; 3 calculating a similar point by defining a geometric local characteristic of a description point, and carry out point-to-point matching; 4 calculating the equidistant distance between the features of the calculated point pairs; 5 classifying that equidistant transformation types of two point in a point pair; 6 comparing equidistant point pairs after equidistant classification; 7 applying that spectral clustering algorithm to obtainthe local correspond similar parts among the objects, and completing the detection of the similar area; 8 judging whether the proportion of the similarity point cloud and the sampling point cloud islarger than the set threshold value to complete the identification of the whole point cloud. The method of the invention realizes efficient and accurate object recognition.

Description

technical field [0001] The invention belongs to the technical field of image processing and graphic transformation, and relates to a three-dimensional point cloud recognition method for graphic processing and similarity detection, in particular to a three-dimensional point cloud recognition method based on B-spline surface similarity detection. Background technique [0002] At present, exploring efficient and robust object shape discrimination algorithms based on feature extraction technology is the core idea in the field of machine vision to solve object recognition problems. At present, there are two main categories of feature extraction methods based on recognition: methods based on global features and methods based on local features. Among these methods, algorithms with high recognition accuracy are often poor in robustness, and their application scenarios are often limited. In point cloud data preprocessing, the steps are cumbersome, resulting in a reduction in recogni...

AI Technical Summary

Problems solved by technology

Among these methods, algorithms with high recognition accuracy are often poor in robustness, and their application scenarios are often limited.

In point cloud data preprocessing, the steps are cumbersome, resulting in a reduction in recognition efficiency

Although there are many recognition methods that can solve their own recognition problems, they do not have the characteristics of accurately describing the geometric shape of the object surface under the premise of completing the recognition.

[0003] After searching, no published patent documents identical or similar to the present invention were found

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