Method

LPCG-Monoflex [LPCG-Monoflex]
https://github.com/SPengLiang/LPCG

Submitted on 31 Aug. 2021 08:30 by
Peng Liang (ZheJiang University)

Running time:0.03 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
Lidar Point Cloud Guided Monocular 3D Object
Detection.
Parameters:
N/A
Latex Bibtex:
@inproceedings{peng2022lidar,
title={Lidar Point Cloud Guided Monocular 3D
Object Detection},
author={Peng, Liang and Liu, Fei and Yu, Zhengxu
and Yan, Senbo and Deng, Dan and Yang, Zheng and
Liu, Haifeng and Cai, Deng},
booktitle={ECCV},
year={2022}
}

Detailed Results

Object detection and orientation estimation results. Results for object detection are given in terms of average precision (AP) and results for joint object detection and orientation estimation are provided in terms of average orientation similarity (AOS).


Benchmark Easy Moderate Hard
Car (Detection) 96.90 % 93.86 % 83.94 %
Car (Orientation) 96.68 % 93.26 % 83.34 %
Car (3D Detection) 25.56 % 17.80 % 15.38 %
Car (Bird's Eye View) 35.96 % 24.81 % 21.86 %
Pedestrian (Detection) 62.44 % 44.13 % 39.46 %
Pedestrian (Orientation) 56.60 % 39.79 % 35.42 %
Pedestrian (3D Detection) 10.82 % 7.33 % 6.18 %
Pedestrian (Bird's Eye View) 12.11 % 7.92 % 6.61 %
Cyclist (Detection) 72.36 % 53.04 % 46.11 %
Cyclist (Orientation) 63.07 % 45.24 % 39.28 %
Cyclist (3D Detection) 6.98 % 4.38 % 3.56 %
Cyclist (Bird's Eye View) 8.14 % 4.90 % 3.86 %
This table as LaTeX


2D object detection results.
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Orientation estimation results.
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3D object detection results.
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Bird's eye view results.
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2D object detection results.
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Orientation estimation results.
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3D object detection results.
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Bird's eye view results.
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2D object detection results.
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Orientation estimation results.
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3D object detection results.
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Bird's eye view results.
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