Method

MonoAMNet: Three-Stage Real-Time Monocular 3D Object Detection With Adaptive Methods [AMNet]
[Anonymous Submission]

Submitted on 28 Nov. 2022 04:14 by
[Anonymous Submission]

Running time:0.03 s
Environment:GPU @ 1.0 Ghz (Python)

Method Description:
Our method does not depend on extra information and
can be trained end-to-end.
Parameters:
g=2000
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) 88.43 % 80.30 % 74.19 %
Car (Orientation) 88.23 % 79.86 % 73.54 %
Car (3D Detection) 26.26 % 19.26 % 17.05 %
Car (Bird's Eye View) 34.68 % 25.40 % 22.85 %
Pedestrian (Detection) 37.11 % 28.50 % 25.83 %
Pedestrian (Orientation) 33.83 % 25.45 % 22.95 %
Pedestrian (3D Detection) 13.18 % 8.67 % 7.43 %
Pedestrian (Bird's Eye View) 14.10 % 9.30 % 8.02 %
Cyclist (Detection) 45.93 % 31.01 % 27.06 %
Cyclist (Orientation) 40.75 % 27.07 % 23.54 %
Cyclist (3D Detection) 4.30 % 2.79 % 2.51 %
Cyclist (Bird's Eye View) 5.54 % 3.61 % 3.19 %
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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