Method

M K With 3D detection [MK3D]


Submitted on 13 Jan. 2022 09:49 by
fang lei (Chongqing University)

Running time:0.03 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
This method mainly integrates a variety of mathematical methods to improve the stability and reliability of 3D detection results, and integrates a variety of results to improve the accuracy of 3D detection
Parameters:
\alpha=0.25
\gamma = 2.0
Latex Bibtex:

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) 94.07 % 83.75 % 71.47 %
Car (Orientation) 92.87 % 81.77 % 69.41 %
Car (3D Detection) 15.39 % 10.37 % 8.82 %
Car (Bird's Eye View) 23.00 % 15.99 % 13.28 %
Pedestrian (Detection) 67.43 % 48.24 % 41.12 %
Pedestrian (Orientation) 59.44 % 42.04 % 35.76 %
Pedestrian (3D Detection) 9.19 % 5.72 % 4.54 %
Pedestrian (Bird's Eye View) 10.15 % 6.78 % 5.71 %
Cyclist (Detection) 62.70 % 40.32 % 35.53 %
Cyclist (Orientation) 50.47 % 31.89 % 27.96 %
Cyclist (3D Detection) 4.16 % 2.50 % 2.02 %
Cyclist (Bird's Eye View) 5.47 % 3.05 % 2.40 %
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.
This figure as: png eps pdf txt gnuplot



3D object detection results.
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Bird's eye view results.
This figure as: png eps pdf txt gnuplot




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