Method

YOLOStereo3D[st] [YOLOStereo3D]
https://github.com/Owen-Liuyuxuan/visualDet3D

Submitted on 31 May. 2020 07:18 by
Yuxuan LIU (Hong Kong University of Science and Technology)

Running time:0.1 s
Environment:GPU 1080Ti

Method Description:
Stereo end2end based on another method
Parameters:
stereo
Latex Bibtex:
@inproceedings{liu2021yolostereo3d,
title={YOLOStereo3D: A Step Back to 2D for
Efficient Stereo 3D Detection},
author={Yuxuan Liu and Lujia Wang and Ming Liu},
booktitle={2021 International Conference on
Robotics and Automation (ICRA)},
year={2021},
organization={IEEE}
}

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) 94.81 % 82.15 % 62.17 %
Car (Orientation) 93.65 % 80.88 % 61.17 %
Car (3D Detection) 65.68 % 41.25 % 30.42 %
Car (Bird's Eye View) 76.10 % 50.28 % 36.86 %
Pedestrian (Detection) 56.20 % 41.46 % 37.07 %
Pedestrian (Orientation) 48.99 % 35.62 % 31.58 %
Pedestrian (3D Detection) 28.49 % 19.75 % 16.48 %
Pedestrian (Bird's Eye View) 31.01 % 20.76 % 18.41 %
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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