3D Object Detection with Normal-map on Point Clouds

Jishu Miao, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi

2021

Abstract

In this paper, we propose a novel point clouds based 3D object detection method for achieving higher-accuracy of autonomous driving. Different types of objects on the road has a different shape. A LiDAR sensor can provide a point cloud including more than ten thousand points reflected from object surfaces in one frame. Recent studies show that hand-crafted features directly extracted from point clouds can achieve nice detection accuracy. The proposed method employs YOLOv4 as feature extractor and gives Normal-map as additional input. Our Normal-map is a three channels bird’s eye view image, retaining detailed object surface normals. It makes the input information have more enhanced spatial shape information and can be associated with other hand-crafted features easily. In an experiment on the KITTI 3D object detection dataset, it performs better than conventional methods. Our method can achieve higher-precision 3D object detection and is less affected by distance. It has excellent yaw angle predictability for the object, especially for cylindrical objects like pedestrians, even if it omits the intensity information.

Paper Citation

in Harvard Style

Miao J., Hirakawa T., Yamashita T. and Fujiyoshi H. (2021). 3D Object Detection with Normal-map on Point Clouds. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP; ISBN 978-989-758-488-6, SciTePress, pages 569-576. DOI: 10.5220/0010304305690576

in Bibtex Style

@conference{visapp21,
author={Jishu Miao and Tsubasa Hirakawa and Takayoshi Yamashita and Hironobu Fujiyoshi},
title={3D Object Detection with Normal-map on Point Clouds},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP},
year={2021},
pages={569-576},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010304305690576},
isbn={978-989-758-488-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP
TI - 3D Object Detection with Normal-map on Point Clouds
SN - 978-989-758-488-6
AU - Miao J.
AU - Hirakawa T.
AU - Yamashita T.
AU - Fujiyoshi H.
PY - 2021
SP - 569
EP - 576
DO - 10.5220/0010304305690576
PB - SciTePress