Method

Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection [MonoDDE]
[Anonymous Submission]

Submitted on 18 Oct. 2021 05:42 by
[Anonymous Submission]

Running time:0.04 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
We have proposed a new method for solving depth in
monocular 3D detection. Without introducing any
extra information, our method achieves promising
results.
Parameters:
lr=3e-4
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) 96.76 % 89.19 % 81.60 %
Car (Orientation) 96.72 % 89.07 % 81.42 %
Car (3D Detection) 24.93 % 17.14 % 15.10 %
Car (Bird's Eye View) 33.58 % 23.46 % 20.37 %
Pedestrian (Detection) 57.80 % 43.36 % 39.00 %
Pedestrian (Orientation) 55.28 % 41.09 % 36.85 %
Pedestrian (3D Detection) 11.13 % 7.32 % 6.67 %
Pedestrian (Bird's Eye View) 12.38 % 8.41 % 7.16 %
Cyclist (Detection) 70.85 % 51.10 % 44.02 %
Cyclist (Orientation) 63.91 % 45.58 % 39.29 %
Cyclist (3D Detection) 5.94 % 3.78 % 3.33 %
Cyclist (Bird's Eye View) 6.68 % 4.36 % 3.76 %
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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