Method

ARPNET(LiDAR-only) [ARPNET]


Submitted on 28 May. 2019 04:28 by
Yangyang Ye (Beijing Jiaotong Univeristy)

Running time:0.08 s
Environment:GPU @ 2.5 Ghz (Python + C/C++)

Method Description:
TBD
Parameters:
TBD
Latex Bibtex:
@Article{Ye2019,
author="Ye, Yangyang
and Zhang, Chi
and Hao, Xiaoli",
title="ARPNET: attention region proposal network
for 3D object detection",
journal="Science China Information Sciences",
year="2019",
month="Nov",
day="05",
volume="62",
number="12",
pages="220104",
issn="1869-1919",
doi="10.1007/s11432-019-2636-x",
url="https://doi.org/10.1007/s11432-019-2636-x"
}

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) 94.00 % 90.99 % 83.49 %
Car (Orientation) 93.42 % 90.11 % 82.56 %
Car (3D Detection) 84.69 % 74.04 % 68.64 %
Car (Bird's Eye View) 90.06 % 86.81 % 79.41 %
Pedestrian (Detection) 69.08 % 56.42 % 52.69 %
Pedestrian (Orientation) 60.47 % 48.49 % 45.02 %
Pedestrian (3D Detection) 48.32 % 39.31 % 35.93 %
Pedestrian (Bird's Eye View) 55.48 % 45.92 % 42.54 %
Cyclist (Detection) 84.96 % 71.95 % 65.21 %
Cyclist (Orientation) 82.61 % 68.72 % 62.00 %
Cyclist (3D Detection) 74.21 % 58.20 % 52.13 %
Cyclist (Bird's Eye View) 82.32 % 66.39 % 58.80 %
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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