@inproceedings{c234b60d9e48483f9366c144799967ff,
title = "An Effective Voxelization Method for LiDAR-based 3D Object Detection",
abstract = "LiDAR-based 3D object detection is crucial for intelligent vehicles to perceive the environment. Voxelization is an important method that can convert unstructured point clouds into structured tensors. However, for LiDARs with different resolutions, what voxel size to use remains to be studied. Therefore, this paper proposes a voxelization method for LiDAR-based 3D object detection. First, the point cloud distribution characteristics of LiDARs with different resolutions are analyzed. Then, a voxelization method adapted to LiDARs with varying resolutions for 3D object detection is proposed. Finally, models based on SECOND and Voxel R-CNN are built and the proposed voxelization method is added to more effectively utilize the point cloud information. Experiments on datasets show that the proposed voxelization method can improve the average precision of the 3D object detection models.",
keywords = "3D object detection, point cloud, voxelization",
author = "Rongxuan Wang and Chao Yang and Weida Wang and Changle Xiang and Ying Li",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 6th CAA International Conference on Vehicular Control and Intelligence, CVCI 2022 ; Conference date: 28-10-2022 Through 30-10-2022",
year = "2022",
doi = "10.1109/CVCI56766.2022.9965035",
language = "English",
series = "2022 6th CAA International Conference on Vehicular Control and Intelligence, CVCI 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 6th CAA International Conference on Vehicular Control and Intelligence, CVCI 2022",
address = "United States",
}