Method

Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection [MonoCon]
https://git.io/MonoCon

Submitted on 8 Sep. 2021 03:36 by
Xianpeng Liu (North Carolina State University)

Running time:0.02 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
This paper proposes a simple yet effective
formulation for monocular 3D object detection
without exploiting any extra information. It
presents the MonoCon method which learns Monocular
Contexts, as auxiliary tasks in training, to help
monocular 3D object detection.
Parameters:
lr=2.25e-4
Latex Bibtex:
@inproceedings{monocon,
title={Learning Auxiliary Monocular Contexts Helps
Monocular 3D Object Detection},
author={Xianpeng Liu, Nan Xue, Tianfu Wu},
booktitle={AAAI},
year={2022}
}

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) 93.59 % 88.22 % 76.18 %
Car (Orientation) 93.52 % 87.92 % 75.83 %
Car (3D Detection) 22.50 % 16.46 % 13.95 %
Car (Bird's Eye View) 31.12 % 22.10 % 19.00 %
Pedestrian (Detection) 56.57 % 42.44 % 36.34 %
Pedestrian (Orientation) 52.16 % 38.67 % 33.15 %
Pedestrian (3D Detection) 13.10 % 8.41 % 6.94 %
Pedestrian (Bird's Eye View) 13.55 % 8.73 % 7.83 %
Cyclist (Detection) 59.39 % 42.49 % 35.94 %
Cyclist (Orientation) 53.26 % 37.33 % 31.67 %
Cyclist (3D Detection) 2.80 % 1.92 % 1.55 %
Cyclist (Bird's Eye View) 3.87 % 2.68 % 2.24 %
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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