Method

Learning Depth-Guided Convolutions for Monocular 3D Object Detection [D4LCN]
https://github.com/dingmyu/D4LCN

Submitted on 1 Nov. 2019 18:09 by
mingyu ding (sensetime)

Running time:0.2 s
Environment:GPU @ 2.5 Ghz (Python + C/C++)

Method Description:
TBD
Parameters:
TBD
Latex Bibtex:
@article{ding2019learning,
title={Learning Depth-Guided Convolutions for
Monocular 3D Object Detection},
author={Ding, Mingyu and Huo, Yuqi and Yi,
Hongwei and Wang, Zhe and Shi, Jianping and Lu,
Zhiwu and Luo, Ping},
journal={CVPR},
year={2020}
}

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) 90.34 % 83.67 % 65.33 %
Car (Orientation) 90.01 % 82.08 % 63.98 %
Car (3D Detection) 16.65 % 11.72 % 9.51 %
Car (Bird's Eye View) 22.51 % 16.02 % 12.55 %
Pedestrian (Detection) 59.55 % 43.50 % 37.12 %
Pedestrian (Orientation) 46.73 % 33.62 % 28.71 %
Pedestrian (3D Detection) 4.55 % 3.42 % 2.83 %
Pedestrian (Bird's Eye View) 5.06 % 3.86 % 3.59 %
Cyclist (Detection) 65.29 % 42.86 % 36.29 %
Cyclist (Orientation) 48.03 % 31.70 % 26.99 %
Cyclist (3D Detection) 2.45 % 1.67 % 1.36 %
Cyclist (Bird's Eye View) 2.72 % 1.82 % 1.79 %
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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