Method

MonoXiver [MonoXiver]
[Anonymous Submission]

Submitted on 12 Nov. 2022 07:05 by
[Anonymous Submission]

Running time:0.03s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
Learning geometric features
Parameters:
alpha=0.5
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) 87.68 % 72.63 % 64.07 %
Car (Orientation) 87.63 % 72.42 % 63.77 %
Car (3D Detection) 25.24 % 19.04 % 16.39 %
Car (Bird's Eye View) 34.14 % 25.37 % 22.20 %
Pedestrian (Detection) 42.82 % 31.80 % 28.71 %
Pedestrian (Orientation) 40.01 % 29.38 % 26.44 %
Pedestrian (3D Detection) 12.70 % 8.32 % 7.04 %
Pedestrian (Bird's Eye View) 13.75 % 8.93 % 7.61 %
Cyclist (Detection) 39.81 % 26.95 % 23.32 %
Cyclist (Orientation) 36.21 % 24.11 % 20.81 %
Cyclist (3D Detection) 3.62 % 2.41 % 2.04 %
Cyclist (Bird's Eye View) 4.66 % 3.17 % 2.69 %
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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