Three-dimensional point cloud classification method fusing persistent coherence

Abstract

The invention belongs to the technical field of computer vision, particularly relates to a three-dimensional point cloud classification method fusing persistent coherence, and aims to solve the problem that point cloud topological features are not represented in the current three-dimensional point cloud classification technology. And further introducing a persistent coherence method in algebraic topology to carry out point cloud classification. The method comprises the following steps: firstly, constructing a point cloud witness simple complex topological structure, and quantifying the persistent coherent topological features of the point cloud from two aspects of Betti number and persistent graph; secondly, defining a loss function based on persistent coherence, and performing training learning on the three-dimensional point cloud classification network model according to the loss function to obtain various parameters of the neural network model; and finally, performing a classification task of the three-dimensional point cloud by using the trained convolutional neural network. Test results show that the point cloud classification accuracy is remarkably improved.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to a three-dimensional point cloud classification method integrating persistent coherence, which is suitable for classifying three-dimensional point clouds. Background technique [0002] With the popularization of 3D scanning equipment such as lidar and depth cameras, the collection of 3D point cloud data is becoming more and more convenient. How to distinguish these point cloud data is a key problem to be solved in the fields of automatic driving, intelligent robot navigation, scene reconstruction, and virtual reality. . 3D point cloud classification refers to classifying point clouds into different point cloud sets, the same point cloud set has similar or the same attributes, and marking the classified point cloud sets with labels, so as to obtain the category of each point cloud set. Traditionally, machine learning methods are generally used for point cloud c...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the problem that the existing 3D point cloud classification method lacks point cloud topological features so that the accuracy is difficult to improve, the present invention proposes a 3D point cloud classification method that integrates persistent homology

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