Method

OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection [OPA-3D]
none

Submitted on 7 Mar. 2022 11:21 by
Guangyao Zhai (Technical University of munich)

Running time:0.04 s
Environment:1 core @ 3.5 Ghz (Python)

Method Description:
Monocular 3D object detection in traffic scenes
Parameters:
\alpha=0.2
Latex Bibtex:
@article{su2023opa,
title={OPA-3D: Occlusion-Aware Pixel-Wise
Aggregation for Monocular 3D Object Detection},
author={Su, Yongzhi and Di, Yan and Zhai,
Guangyao and Manhardt, Fabian and Rambach, Jason
and Busam, Benjamin and Stricker, Didier and
Tombari, Federico},
journal={IEEE Robotics and Automation Letters},
year={2023},
publisher={IEEE}
}

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) 96.50 % 88.77 % 76.55 %
Car (Orientation) 96.41 % 88.44 % 76.17 %
Car (3D Detection) 24.60 % 17.05 % 14.25 %
Car (Bird's Eye View) 33.54 % 22.53 % 19.22 %
Pedestrian (Detection) 73.93 % 54.92 % 47.87 %
Pedestrian (Orientation) 68.35 % 50.42 % 43.91 %
Pedestrian (3D Detection) 15.65 % 10.49 % 8.80 %
Pedestrian (Bird's Eye View) 17.14 % 11.01 % 9.94 %
Cyclist (Detection) 55.98 % 38.35 % 33.83 %
Cyclist (Orientation) 45.97 % 31.64 % 27.92 %
Cyclist (3D Detection) 5.16 % 3.45 % 2.86 %
Cyclist (Bird's Eye View) 6.01 % 3.75 % 3.56 %
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.
This figure as: png eps pdf txt gnuplot




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