Method

LVP [LVP]


Submitted on 28 Nov. 2024 09:49 by
mingqi wang (palomar college)

Running time:0.04 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
multi-modal
Parameters:
virtual points/points
Latex Bibtex:
@article{chen2024lvp,
title={LVP: Leverage Virtual Points in Multi-
modal Early Fusion for 3D Object Detection},
author={Chen, Yidong and Cai, Guorong and Song,
Ziying and Liu, Zhaoliang and Zeng, Binghui and
Li, Jonathan and Wang, Zongyue},
journal={IEEE Transactions on Geoscience and
Remote Sensing},
year={2024},
publisher={IEEE}
}

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) 98.70 % 97.84 % 93.07 %
Car (Orientation) 98.68 % 97.66 % 92.81 %
Car (3D Detection) 91.37 % 84.92 % 80.07 %
Car (Bird's Eye View) 95.49 % 91.80 % 88.91 %
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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