Method

DDMP-3D [DDMP-3D]


Submitted on 16 Nov. 2020 11:16 by
Lucas (Fudan)

Running time:0.18 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
TBD
Parameters:
Please refer to paper for details.
Latex Bibtex:
@inproceedings{ddmp3d,
title={Depth-conditioned Dynamic Message Propagation for
Monocular 3D Object Detection},
author={Wang, Li and Du, Liang and Ye, Xiaoqing and Fu, Yanwei
and Guo, Guodong and Xue, Xiangyang and Feng, Jianfeng and
Zhang, Li},
booktitle={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) 91.15 % 81.70 % 63.12 %
Car (Orientation) 90.73 % 80.20 % 61.82 %
Car (3D Detection) 19.71 % 12.78 % 9.80 %
Car (Bird's Eye View) 28.08 % 17.89 % 13.44 %
Pedestrian (Detection) 56.73 % 41.54 % 35.52 %
Pedestrian (Orientation) 46.12 % 33.35 % 28.45 %
Pedestrian (3D Detection) 4.93 % 3.55 % 3.01 %
Pedestrian (Bird's Eye View) 5.53 % 4.02 % 3.36 %
Cyclist (Detection) 58.70 % 38.62 % 34.10 %
Cyclist (Orientation) 46.42 % 29.53 % 25.91 %
Cyclist (3D Detection) 4.18 % 2.50 % 2.32 %
Cyclist (Bird's Eye View) 4.92 % 3.14 % 2.44 %
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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