3D point cloud simplification algorithm combined with human visual perception characteristics

Abstract

The invention discloses a 3D point cloud simplification algorithm combining human visual perception characteristics, belongs to the field of 3D point cloud data processing, and provides a 3D point cloud simplification algorithm combining geometric characteristics with human visual perception characteristics in order to solve the problem that later data processing, storage and transmission burdens are aggravated due to sharp increase of dense 3D point cloud data volume. In combination with geometric features of the point cloud, the algorithm establishes a one-way perception sharpness function and a local visibility function to complete importance evaluation of points, and then different simplification rules are formulated according to the importance of the points to realize hierarchical simplification of the point cloud. Besides, in order to improve the universality of the hybrid feature evaluation model, a dynamic weight optimization strategy of each evaluation function is established, and real-time weight value updating with feature evaluation results as guidance is realized. Experiments verify the effectiveness of the provided algorithm, and compared with a traditional point cloud simplification algorithm, the algorithm of the invention can keep the local details of the point cloud to the greatest extent while keeping the overall uniformity of data.

Description

technical field [0001] The invention belongs to the field of 3D point cloud data processing, and in particular relates to a 3D point cloud reduction algorithm combined with human visual perception characteristics. Background technique [0002] The rapid development of 3D reconstruction technology has laid a foundation for the acquisition of 3D point clouds. At the same time, the continuous improvement of the accuracy and density of 3D point clouds has made it more widely used in tasks such as 3D printing, online detection, and target recognition. According to different application requirements, all kinds of dense 3D point clouds have different degrees of information redundancy. Using a reasonable 3D point cloud reduction technology can effectively improve the processing, storage and transmission efficiency of 3D data in the later stage. Therefore, the 3D point cloud reduction algorithm is studied. It has become a hot topic in the field of data processing. [0003] Existing ...

AI Technical Summary

Problems solved by technology

Although these algorithms use different feature evaluation indicators, they all focus on retaining the significant geometric features of the point cloud model, ignoring the sensitive areas of human vision to point clouds, and the weight of each feature evaluation index is usually set according to experience. When the shape of the streamlined point cloud changes greatly, the imbalance of weight values ​​will reduce the later application value of the point cloud

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