Method

Learning-based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection [st] [RT3D-GMP]


Submitted on 20 Feb. 2021 21:19 by
Hendrik Königshof (KIT)

Running time:0.06 s
Environment:GPU @ 2.5 Ghz (Python + C/C++)

Method Description:
RT3D-GMP
Parameters:
RT3D-GMP
Latex Bibtex:
@inproceedings{konigshof2020learning,
title={Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving},
author={Königshof, Hendrik and Stiller, Christoph},
booktitle={2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)},
pages={1--6},
year={2020},
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) 62.41 % 51.95 % 39.14 %
Car (Orientation) 44.06 % 36.31 % 27.32 %
Car (3D Detection) 45.79 % 38.76 % 30.00 %
Car (Bird's Eye View) 69.14 % 59.00 % 45.49 %
Pedestrian (Detection) 55.56 % 39.83 % 35.18 %
Pedestrian (Orientation) 40.81 % 28.75 % 25.13 %
Pedestrian (3D Detection) 16.23 % 11.41 % 10.12 %
Pedestrian (Bird's Eye View) 19.92 % 14.22 % 12.83 %
Cyclist (Detection) 33.64 % 22.90 % 19.87 %
Cyclist (Orientation) 23.91 % 16.18 % 14.23 %
Cyclist (3D Detection) 18.31 % 12.99 % 10.63 %
Cyclist (Bird's Eye View) 20.59 % 13.92 % 12.74 %
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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