Construction method of multi-plane coding point cloud feature deep learning model based on pointpillars

Abstract

The invention belongs to the technical field of computer vision, and particularly discloses a construction method of a multi-plane coding point cloud feature deep learning model based on pointpillars.The construction method comprises the following steps of: acquiring training samples, and training a multi-plane coding point cloud feature deep learning model by adopting the training samples, so that a recognition result obtained by inputting the point cloud data in the training samples into the trained multi-plane coding point cloud feature deep learning model is the position bounding box coordinates of the detection target in the point cloud data and the existence probability of the target in the bounding box coordinates. Three-dimensional space sampling of point cloud data can be realized; the method is advantaged in that the support point cloud features in the three planes are acquired through sampling, learning fusion is carried out on the support point cloud features in the threeplanes, a problem of space information loss of point cloud sampling in the prior art is solved, detection precision loss caused by different angles of the point cloud in each direction in the space isbetter reduced, model robustness is good, and detection accuracy is high.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to a construction method of a pointpillars-based multi-plane coded point cloud feature deep learning model. Background technique [0002] Target detection is an important task of computer vision. The purpose is to identify the type of target and locate the position of the target. For the traditional two-dimensional target detection, the field of computer vision has been very mature. The three-dimensional object detection is aimed at the image level and only contains the plane information of the object. With the rapid development of the autonomous driving industry, object detection pays more and more attention to the three-dimensional information of the object, so the three-dimensional object detection technology based on deep learning has also been developed rapidly. , the current 3D object detection technology mainly relies on images and lidar point clouds for e...

AI Technical Summary

Problems solved by technology

The complex and changeable application scenarios in areas such as unmanned driving have obvious limitations on the traditional two-dimensional target detection algorithm. In order to improve the accuracy of detection and ensure the safety of drivers, the accuracy and speed of three-dimensional target detection are It is a big challenge, but the unmanned driving scene is empty, and the point cloud collected by the lidar is not uniform, the collected distant points are very sparse, and the deep learning method of the spatial point cloud needs to contain complete spatial information

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