Method

RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving [RefinedMPL]


Submitted on 22 Nov. 2019 19:55 by
Jean Marie Uwabeza Vianney (University of Waterloo)

Running time:0.15 s
Environment:GPU @ 2.5 Ghz (Python + C/C++)

Method Description:
TBA
Parameters:
Latex Bibtex:
@article{vianney2019refinedmpl,
title={RefinedMPL: Refined Monocular PseudoLiDAR
for 3D Object Detection in Autonomous Driving},
author={Vianney, Jean Marie Uwabeza and Aich,
Shubhra and Liu, Bingbing},
journal={arXiv preprint arXiv:1911.09712},
year={2019}
}

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) 88.29 % 65.24 % 53.20 %
Car (Orientation) 87.95 % 64.02 % 52.06 %
Car (3D Detection) 18.09 % 11.14 % 8.94 %
Car (Bird's Eye View) 28.08 % 17.60 % 13.95 %
Pedestrian (Detection) 30.41 % 20.81 % 18.72 %
Pedestrian (Orientation) 25.83 % 17.26 % 15.41 %
Pedestrian (3D Detection) 11.14 % 7.18 % 5.84 %
Pedestrian (Bird's Eye View) 13.09 % 7.92 % 7.25 %
Cyclist (Detection) 44.47 % 27.17 % 22.84 %
Cyclist (Orientation) 26.54 % 16.02 % 13.20 %
Cyclist (3D Detection) 3.23 % 1.82 % 1.77 %
Cyclist (Bird's Eye View) 4.23 % 2.42 % 2.14 %
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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