Method

Monocular 3D Object Detection with Feature Enhancement Networks [MonoFENet]


Submitted on 6 May. 2019 14:46 by
Wentao Bao (Wuhan University)

Running time:0.15 s
Environment:1 core @ 3.5 Ghz (Python)

Method Description:
An extension of our previous CVPR2018 paper:
Multi-Level Fusion based 3D Object Detection from
Monocular Images.
Parameters:
N/A
Latex Bibtex:
@article{monofenet,
title={MonoFENet: Monocular 3D Object
Detection
with Feature Enhancement Networks},
author={Wentao Bao and Bin Xu and Zhenzhong
Chen},
journal={IEEE Transactions on Image
Processing},
doi={10.1109/TIP.2019.2952201},
year={2019}
}

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) 91.68 % 84.63 % 76.71 %
Car (Orientation) 91.42 % 84.09 % 75.93 %
Car (3D Detection) 8.35 % 5.14 % 4.10 %
Car (Bird's Eye View) 17.03 % 11.03 % 9.05 %
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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