Method

VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion [VPFNet]
https://github.com/zhukevkesky/VPFNet

Submitted on 14 May. 2021 15:15 by
Hanqi Zhu (University of Science and Technology of China)

Running time:0.06 s
Environment:2 cores @ 2.5 Ghz (Python)

Method Description:
TBD
Parameters:
TBD
Latex Bibtex:
@ARTICLE{9826439,
author={Zhu, Hanqi and Deng, Jiajun and Zhang,
Yu and Ji, Jianmin and Mao, Qiuyu and Li, Houqiang
and Zhang, Yanyong},
journal={IEEE Transactions on Multimedia},
title={VPFNet: Improving 3D Object Detection
with Virtual Point based LiDAR and Stereo Data
Fusion},
year={2022},
volume={},
number={},
pages={1-14},
doi={10.1109/TMM.2022.3189778}}

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) 96.64 % 96.15 % 91.14 %
Car (Orientation) 96.63 % 96.04 % 90.99 %
Car (3D Detection) 91.02 % 83.21 % 78.20 %
Car (Bird's Eye View) 93.02 % 91.86 % 86.94 %
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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