Method

Anonymous [Anonymous]
[Anonymous Submission]

Submitted on 13 Mar. 2023 03:12 by
[Anonymous Submission]

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
Anonymous Submission
Parameters:
Our network is trained by SGD with an
initial learning rate 0.002, momentum 0.9, and
weight decay 0.001 respectively.
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) 96.66 % 93.57 % 90.87 %
Car (Orientation) 96.63 % 93.43 % 90.63 %
Car (3D Detection) 88.40 % 82.29 % 77.63 %
Car (Bird's Eye View) 92.84 % 89.47 % 84.65 %
Pedestrian (Detection) 68.51 % 57.12 % 53.06 %
Pedestrian (Orientation) 64.01 % 52.15 % 48.24 %
Pedestrian (3D Detection) 51.16 % 42.72 % 39.06 %
Pedestrian (Bird's Eye View) 55.23 % 46.48 % 42.67 %
Cyclist (Detection) 88.37 % 76.98 % 70.15 %
Cyclist (Orientation) 88.12 % 76.60 % 69.77 %
Cyclist (3D Detection) 82.06 % 66.14 % 58.06 %
Cyclist (Bird's Eye View) 82.82 % 69.19 % 62.55 %
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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