Method

Monocular 3D Object Detection via Point Cloud Network Simulation [MonoPCNS]


Submitted on 15 Aug. 2022 19:13 by
Feiyi FANG (Nanjing University Of Science And Technology)

Running time:0.14 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
pending
Parameters:
pending
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) 92.57 % 85.56 % 78.41 %
Car (Orientation) 92.25 % 84.91 % 77.54 %
Car (3D Detection) 20.31 % 13.74 % 12.31 %
Car (Bird's Eye View) 28.27 % 19.89 % 17.96 %
Pedestrian (Detection) 49.77 % 36.61 % 33.01 %
Pedestrian (Orientation) 35.57 % 25.14 % 22.42 %
Pedestrian (3D Detection) 14.16 % 8.63 % 7.30 %
Pedestrian (Bird's Eye View) 15.56 % 9.65 % 8.27 %
Cyclist (Detection) 31.31 % 22.75 % 20.12 %
Cyclist (Orientation) 20.68 % 14.44 % 12.96 %
Cyclist (3D Detection) 4.07 % 2.09 % 2.12 %
Cyclist (Bird's Eye View) 4.65 % 2.46 % 2.42 %
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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