Method

Triangulation Learning Network: from Monocular to Stereo 3D Object Detection [st] [TLNet (Stereo)]
https://github.com/Zengyi-Qin/TLNet

Submitted on 10 Apr. 2020 09:34 by
Kim Qin (Stanford University)

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
Stereo
Parameters:
Stereo
Latex Bibtex:
@article{qin2019tlnet,
title={Triangulation Learning Network: from
Monocular to Stereo 3D Object Detection},
author={Zengyi Qin and Jinglu Wang and Yan Lu},
journal={IEEE Conference on Computer Vision and
Pattern Recognition (CVPR)},
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) 76.92 % 63.53 % 54.58 %
Car (3D Detection) 7.64 % 4.37 % 3.74 %
Car (Bird's Eye View) 13.71 % 7.69 % 6.73 %
This table as LaTeX

2D object detection results.
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3D object detection results.
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Bird's eye view results.
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