LiDAR-camera fusion for multi-modal 3D object detection

Chunling Liu; Hai Yang; Wenjun Kao; Jianguo Bai

doi:10.1117/12.3004702

9 October 2023 LiDAR-camera fusion for multi-modal 3D object detection

Chunling Liu, Hai Yang, Wenjun Kao, Jianguo Bai

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 127912G (2023) https://doi.org/10.1117/12.3004702
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

Abstract

With the continuous development of autonomous driving technology, more and more vehicles are equipped with different kinds of sensors for environmental sensing. Among these sensors, LiDAR and camera are the most visible, but there are great data structure differences between the point cloud data of LiDAR and RGB image data of camera, which bring some difficulties to the fusion. To address the above issues, this paper proposes a multimodal fusion framework CAD-Net for LiDAR-Camera fusion for 3D object detection. CAD-Net demonstrates the significant performance improvement of the proposed method by conducting comparative experiments and correlation analysis on KITTI public dataset.

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

Citation Download Citation

Chunling Liu, Hai Yang, Wenjun Kao, and Jianguo Bai "LiDAR-camera fusion for multi-modal 3D object detection", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 127912G (9 October 2023); https://doi.org/10.1117/12.3004702

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KEYWORDS

Point clouds

Object detection

LIDAR

Cameras

Education and training

Image fusion

Feature extraction

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