Method

Pseudo-Stereo for Monocular 3D object Detection [PS-fld]
https://github.com/revisitq/Pseudo-Stereo-3D

Submitted on 18 Nov. 2021 06:20 by
Hang Dai (Mohamed bin Zayed University of Artificial Intelligence)

Running time:0.25 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
Pseudo-Stereo for Monocular 3D Object Detection in
Autonomous Driving. Accepted to CVPR 2022.
Parameters:
batch size 4
Latex Bibtex:
@InProceedings{Chen_2022_CVPR,
title={Pseudo-Stereo for Monocular 3D Object
Detection in Autonomous Driving},
author={Yi-Nan Chen and Hang Dai and Yong Ding},
booktitle = {Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern
Recognition (CVPR)},
month = {June},
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) 95.75 % 90.27 % 82.32 %
Car (Orientation) 95.60 % 89.78 % 81.68 %
Car (3D Detection) 23.74 % 17.74 % 15.14 %
Car (Bird's Eye View) 32.64 % 23.76 % 20.64 %
Pedestrian (Detection) 55.47 % 40.47 % 36.65 %
Pedestrian (Orientation) 32.84 % 23.67 % 21.40 %
Pedestrian (3D Detection) 16.95 % 10.82 % 9.26 %
Pedestrian (Bird's Eye View) 19.03 % 12.23 % 10.53 %
Cyclist (Detection) 58.13 % 41.13 % 35.90 %
Cyclist (Orientation) 41.21 % 27.99 % 24.75 %
Cyclist (3D Detection) 11.22 % 6.18 % 5.21 %
Cyclist (Bird's Eye View) 12.80 % 7.29 % 6.05 %
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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