Method

Aug3D-RPN [Aug3D-RPN]


Submitted on 17 Mar. 2021 04:10 by
Chenhang HE (The Hong Kong Polytechnic University)

Running time:0.08 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
Improving 3D object detection with virtual-depth
images
Parameters:
TBD
Latex Bibtex:
@misc{he2021aug3drpn,
title={Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth},
author={Chenhang He and Jianqiang Huang and Xian-Sheng Hua and Lei Zhang},
year={2021},
eprint={2107.13269},
archivePrefix={arXiv},
primaryClass={cs.CV}
}

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) 85.57 % 77.88 % 61.16 %
Car (Orientation) 84.89 % 76.89 % 60.21 %
Car (3D Detection) 17.82 % 12.99 % 9.78 %
Car (Bird's Eye View) 26.00 % 17.89 % 14.18 %
Pedestrian (Detection) 47.22 % 34.95 % 30.64 %
Pedestrian (Orientation) 40.50 % 29.75 % 25.96 %
Pedestrian (3D Detection) 6.01 % 4.71 % 3.87 %
Pedestrian (Bird's Eye View) 7.14 % 5.22 % 4.21 %
Cyclist (Detection) 51.49 % 36.69 % 30.04 %
Cyclist (Orientation) 42.60 % 30.01 % 24.74 %
Cyclist (3D Detection) 4.36 % 2.43 % 2.55 %
Cyclist (Bird's Eye View) 5.44 % 3.33 % 2.82 %
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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