Three-dimensional point cloud semantic segmentation method and device, equipment and medium

Abstract

The invention relates to the technical field of artificial intelligence, and discloses a three-dimensional point cloud semantic segmentation method, device and equipment and a medium, and the method comprises the steps: carrying out the point cloud division and quantitative discrimination of to-be-predicted three-dimensional point cloud data through employing a preset space cell, and obtaining target point cloud data; inputting the target point cloud data into a point cloud semantic category prediction model to perform semantic category probability prediction to obtain a point cloud semantic category probability prediction value of the target point cloud data, wherein the point cloud semantic category prediction model is a model obtained by training based on a Point SIFT neural network module and a Point Net + + neural network; and determining a target semantic category of each point in the target point cloud data according to the point cloud semantic category probability prediction value. According to the method, rapid and accurate logic division is carried out on the point cloud of the complex large-scale target object, the recognition precision of point cloud segmentation is improved, fine features of the complex target object can be well processed, and the accuracy of semantic category prediction is improved.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, in particular to a method, device, equipment and medium for semantic segmentation of 3D point clouds. Background technique [0002] In recent years, with the rise of intelligent applications based on point clouds such as autonomous driving, medical diagnosis, augmented and mixed reality, the research and application of 3D point cloud semantic segmentation technology in deep learning is particularly urgent and important. The existing 3D point cloud semantic segmentation technology includes: deep learning segmentation technology using voxel method, deep learning segmentation technology using multi-view method, and deep learning segmentation technology using point cloud method. [0003] The deep learning segmentation technology using the voxel method, because when the voxel data represents the object, in order to ensure the integrity of the target information, it often has ...

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Problems solved by technology

[0006] The main purpose of this application is to provide a 3D point cloud semantic segmentation method, device, equipment and medium, aiming to solve the technica

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