Method

QD-3DT [on] [QD-3DT]
https://eborboihuc.github.io/QD-3DT

Submitted on 8 Dec. 2020 11:42 by
Hou-Ning Hu (Berkeley )

Running time:0.03 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
End-to-end 3D detection and tracking
Parameters:
None
Latex Bibtex:
@article{Hu2021QD3DT,
author = {Hu, Hou-Ning and Yang, Yung-Hsu and
Fischer, Tobias and Yu, Fisher and Darrell, Trevor
and Sun, Min},
title = {Monocular Quasi-Dense 3D Object Tracking},
journal = {ArXiv:2103.07351},
year = {2021}
}

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) 94.26 % 93.66 % 83.63 %
Car (Orientation) 92.61 % 90.49 % 80.32 %
Car (3D Detection) 12.81 % 9.33 % 7.86 %
Car (Bird's Eye View) 20.16 % 14.71 % 12.76 %
Pedestrian (Detection) 78.41 % 59.26 % 54.37 %
Pedestrian (Orientation) 68.64 % 51.46 % 47.00 %
Pedestrian (3D Detection) 5.53 % 3.37 % 3.02 %
Pedestrian (Bird's Eye View) 6.62 % 4.23 % 3.39 %
Cyclist (Detection) 75.55 % 56.51 % 49.70 %
Cyclist (Orientation) 64.64 % 46.24 % 40.58 %
Cyclist (3D Detection) 4.16 % 2.39 % 1.85 %
Cyclist (Bird's Eye View) 5.71 % 3.02 % 2.73 %
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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