Three-dimensional grid object segmentation method based on point cloud processing network

Abstract

The invention discloses a three-dimensional grid object segmentation method based on a point cloud processing network. The three-dimensional grid object segmentation method comprises the following steps: preparing a three-dimensional grid data set and a pre-trained point cloud processing network PointNet; calculating a central point coordinate of a triangular patch in the three-dimensional grid data as point cloud data corresponding to the three-dimensional grid data; randomly selecting N points in the point cloud, and inputting the N points into a pre-trained Point Net to obtain a segmentation result; giving probability distribution to unselected points in the point cloud by using a KNN algorithm to obtain all segmented point cloud data; and mapping the point cloud segmentation result back to the three-dimensional grid data, and correcting the result by using a conditional random field to finally obtain a segmentation result of the three-dimensional grid. On the basis of deep learning, complete data can be analyzed, global features are extracted from the data, and data-driven segmentation is achieved instead of a traditional three-dimensional feature extraction operator based on artificial design.

Description

technical field [0001] The invention relates to three-dimensional model segmentation and point cloud feature extraction technology in the computer field, and relates to a three-dimensional grid object segmentation method based on a point cloud processing network. Background technique [0002] With the increasing availability of 3D data, data-driven approaches are becoming increasingly applicable to 3D shape processing due to the development of 3D sensing technology and 3D modeling software. 3D data processing tasks mainly include SLAM 3D modeling, 3D object detection, style transfer, human pose estimation, bone tracking and so on. Among them, 3D object detection and 3D modeling are inseparable from the recognition of 3D objects, and the style transfer of 3D objects, human posture, estimation, and bone tracking all require segmentation based on 3D data, or an excellent 3D data segmentation algorithm is beneficial to other 3D data processing tasks. Among many processing task...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to provide a 3D mesh object segmentation method based on a point cloud processing network for the existing segmentation models that are mostly limited to processing a single surface, and it is difficult to directly extract features from complete data. The method can extract features from the complete 3D mesh model under the premise of ensuring the segmentation effect, and directly process the complete 3D mesh data

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