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Authors: Jishu Miao ; Tsubasa Hirakawa ; Takayoshi Yamashita and Hironobu Fujiyoshi

Affiliation: Chubu University, 1200 Matsumoto-cho, Kasugai, Aichi, Japan

Keyword(s): Object Detection, Deep Learning, Point Cloud Processing, Autonomous Vehicles.

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 ya w angle predictability for the object, especially for cylindrical objects like pedestrians, even if it omits the intensity information. (More)

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Paper citation in several formats:
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; ISSN 2184-4321, SciTePress, pages 569-576. DOI: 10.5220/0010304305690576

@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},
issn={2184-4321},
}

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
IS - 2184-4321
AU - Miao, J.
AU - Hirakawa, T.
AU - Yamashita, T.
AU - Fujiyoshi, H.
PY - 2021
SP - 569
EP - 576
DO - 10.5220/0010304305690576
PB - SciTePress

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