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{yangyangarpnet,
title={ARPNET: Attention Regional Proposal Network
for 3D Object Detection},
author={Yangyang, Ye and Chi, Zhang and Xiaoli,
Hao},
journal={SCIENCE CHINA Information Sciences},
publisher={Science China Press}
}

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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