Method

GLENet-VR [GLENet-VR]
https://github.com/Eaphan/GLENet

Submitted on 29 Mar. 2022 09:42 by
Yifan Zhang (CMC)

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

Method Description:
GLENet: Boosting 3D Object Detectors with Generative
Label Uncertainty Estimation
Parameters:
TODO
Latex Bibtex:
@article{zhang2023glenet,
title={GLENet: Boosting 3D object
detectors
with generative label uncertainty estimation},
author={Zhang, Yifan and Zhang, Qijian and
Zhu, Zhiyu and Hou, Junhui and Yuan, Yixuan},
journal={International Journal of Computer
Vision},
pages={1--21},
year={2023},
}
@article{zhang2023comprehensive,
title={A Comprehensive Study of the Robustness
for LiDAR-based 3D Object Detectors against
Adversarial Attacks},
author={Zhang, Yifan and Hou, Junhui and Yuan,
Yixuan},
journal={International Journal of Computer
Vision},
year={2023}
}

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) 96.85 % 95.81 % 90.91 %
Car (Orientation) 96.84 % 95.73 % 90.80 %
Car (3D Detection) 91.67 % 83.23 % 78.43 %
Car (Bird's Eye View) 93.48 % 89.76 % 84.89 %
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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