Method

monodle [monodle]
https://github.com/xinzhuma/monodle

Submitted on 13 Nov. 2020 07:47 by
yuan meng (tsinghua university)

Running time:0.04 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
Anonymous submission. Method and code will be
released upon acceptance.
Parameters:
none
Latex Bibtex:
@inproceedings{MA_2021_CVPR,
author = {Xinzhu Ma and Yinmin Zhang and Dan Xu
and Dongzhan Zhou and Shuai Yi and Haojie Li and
Wanli Ouyang},
title = {Delving into Localization Errors for
Monocular 3D Object Detection},
booktitle = {CVPR},
address = { Nashville, TN },
month = { June },
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) 93.83 % 90.81 % 80.93 %
Car (Orientation) 93.46 % 90.23 % 80.11 %
Car (3D Detection) 17.23 % 12.26 % 10.29 %
Car (Bird's Eye View) 24.79 % 18.89 % 16.00 %
Pedestrian (Detection) 78.66 % 61.29 % 56.18 %
Pedestrian (Orientation) 69.94 % 53.78 % 48.98 %
Pedestrian (3D Detection) 9.64 % 6.55 % 5.44 %
Pedestrian (Bird's Eye View) 10.73 % 6.96 % 6.20 %
Cyclist (Detection) 70.78 % 53.29 % 45.01 %
Cyclist (Orientation) 61.84 % 45.12 % 37.95 %
Cyclist (3D Detection) 4.59 % 2.66 % 2.45 %
Cyclist (Bird's Eye View) 5.34 % 3.28 % 2.83 %
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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