Point cloud data labeling method and device

Abstract

The embodiment of the invention discloses a point cloud data labeling method and device. The method comprises the following steps: obtaining a to-be-labeled point cloud data frame and corresponding acquisition equipment pose information; obtaining a to-be-displayed point cloud data frame corresponding to the labeled point cloud data frame, and displaying the to-be-displayed point cloud data frame; after a first selection operation is detected, determining first labeling box information corresponding to a to-be-labeled target; determining the current superposition frame of a superposition frame number information frame after or before the current display frame; displaying the current display frame and the current superposition frame in a superposition manner based on the current display frame and the acquisition equipment pose information corresponding to the current display frame; after a second selection operation is detected, determining second labeling box information corresponding to a to-be-labeled target in a target frame of the current superposition frame; and based on the first labeling box information and the second labeling box information, determining third labeling box information corresponding to the to-be-labeled target in each current superposition frame between the current display frame and the target frame, so that simple, convenient and effective labeling of the point cloud data is realized, the burden of labeling personnel is reduced, and the efficiency is improved.

Description

technical field [0001] The present invention relates to the technical field of data labeling, in particular to a method and device for labeling point cloud data. Background technique [0002] The training of the 3D target detection algorithm based on deep learning relies on a large amount of labeled 3D lidar point cloud data, where the 3D lidar point cloud data is the point cloud data collected by lidar. In related technologies, the labeling of 3D lidar point cloud data is generally done manually. Compared with the labeling of 2D image data, the manual labeling process of 3D lidar point cloud data is cumbersome, slow and costly. [0003] In the scene of manual labeling of 3D lidar point cloud data of continuous multiple frames, the labeler needs to label the 3D laser radar point cloud data frame by frame. In this scenario, the labeler not only needs to pay attention to each label box In itself, it is also necessary to refer to the 3D lidar point cloud data of several frame...

AI Technical Summary

Problems solved by technology

In some scenarios, it is also necessary to ensure that the labeling boxes with related relations on different frames correspond to the same physical object, and keep consistent in some attribute information. These requirements have brought about the improvement of the labeling efficiency of 3D lidar point cloud data. had a greater impact

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