Method

Intra-and-Inter-Plane-Constraints [Plane-Constraints]
https://github.com/DL-YHD/Intra-and-Inter-Plane-Constraints

Submitted on 7 Mar. 2023 02:42 by
Hongdou Yao (Wuhan University)

Running time:0.05 s
Environment:4 cores @ 3.0 Ghz (Python)

Method Description:
Vertex points are not enough: Monocular 3D object
detection via intraand inter-plane constraints
Parameters:
model parameters = 82.861MB
Latex Bibtex:
@article{yao2023vertex,
title={Vertex points are not enough: Monocular
3D object detection via intra-and inter-plane
constraints},
author={Yao, Hongdou and Chen, Jun and Wang,
Zheng and Wang, Xiao and Chai, Xiaoyu and Qiu,
Yansheng and Han, Pengfei},
journal={Neural Networks},
year={2023},
publisher={Elsevier}
}

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) 82.54 % 75.43 % 66.82 %
Car (Orientation) 82.46 % 75.18 % 66.51 %
Car (3D Detection) 11.29 % 7.88 % 6.48 %
Car (Bird's Eye View) 17.31 % 12.06 % 10.05 %
Pedestrian (Detection) 64.64 % 51.57 % 46.98 %
Pedestrian (Orientation) 57.28 % 44.76 % 40.56 %
Pedestrian (3D Detection) 1.73 % 1.09 % 1.04 %
Pedestrian (Bird's Eye View) 1.87 % 1.16 % 1.13 %
Cyclist (Detection) 58.71 % 41.01 % 35.35 %
Cyclist (Orientation) 48.36 % 32.87 % 28.52 %
Cyclist (3D Detection) 7.72 % 4.22 % 3.36 %
Cyclist (Bird's Eye View) 8.67 % 4.79 % 3.90 %
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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