Method

Intensive Point-based Object Detector for Point Cloud [IPOD]
[Anonymous Submission]

Submitted on 17 Nov. 2018 08:38 by
[Anonymous Submission]

Running time:0.2 s
Environment:GPU @ 2.5 Ghz (Python + C/C++)

Method Description:
TBD
Parameters:
TBD
Latex Bibtex:
@article{IPOD,
title={IPOD: Intensive Point-based Object Detector for Point
Cloud},
author={Zetong Yang and Yanan Sun and Shu Liu and
Xiaoyong Shen and Jiaya Jia},
journal={arXiv preprint arXiv:1812.05276},
year={2018}
}

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) 90.20 % 89.30 % 87.37 %
Car (Orientation) 36.95 % 37.01 % 36.96 %
Car (3D Detection) 79.75 % 72.57 % 66.33 %
Car (Bird's Eye View) 86.93 % 83.98 % 77.85 %
Pedestrian (Detection) 73.28 % 63.07 % 56.71 %
Pedestrian (Orientation) 41.46 % 35.32 % 31.59 %
Pedestrian (3D Detection) 56.92 % 44.68 % 42.39 %
Pedestrian (Bird's Eye View) 60.83 % 51.24 % 45.40 %
Cyclist (Detection) 82.90 % 65.28 % 57.63 %
Cyclist (Orientation) 36.04 % 28.88 % 25.66 %
Cyclist (3D Detection) 71.40 % 53.46 % 48.34 %
Cyclist (Bird's Eye View) 77.10 % 58.92 % 51.01 %
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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