Lidar 3D object semi-automatic annotation tool

Simin Yu; Xinchen Zhang

doi:10.1117/12.3006203

10 October 2023 Lidar 3D object semi-automatic annotation tool

Simin Yu, Xinchen Zhang

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127993O (2023) https://doi.org/10.1117/12.3006203
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

The environment sensing module is a prerequisite for autonomous driving. The selection of on-board sensors and a good 3D object detection algorithm determine the performance of the environment sensing module. The LIDAR point cloud 3D object semi-automatic annotation tool uses the detection algorithm in the open source framework MMDtection3D to complete the pre-labeling, and then enters the joint point cloud image labeling tool 3D-BAT to complete the manual correction after the data processing module, and the output labeling results can be supplied to the deep learning based 3D object detection algorithm training after the format conversion. The self-built data acquisition vehicle equipped with 6 cameras and 1 LIDAR collected more than 4000 frames of point cloud data, and the semi-automatic annotation tool completed the annotation of more than 2000 frames of point cloud data.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Simin Yu and Xinchen Zhang "Lidar 3D object semi-automatic annotation tool", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127993O (10 October 2023); https://doi.org/10.1117/12.3006203

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