Method

MonoNeRD [MonoNeRD]
https://github.com/cskkxjk/MonoNeRD

Submitted on 2 Nov. 2022 07:45 by
Junkai Xu (Zhejiang university)

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

Method Description:
TBD
Parameters:
TBD
Latex Bibtex:
@inproceedings{xu2023mononerd,
title={MonoNeRD: NeRF-like Representations for
Monocular 3D Object Detection},
author={Xu, Junkai and Peng, Liang and Cheng, Haoran
and Li, Hao and Qian, Wei and Li, Ke and Wang,
WenXiao and Cai, Deng},
booktitle={ICCV},
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) 94.60 % 86.89 % 77.23 %
Car (Orientation) 94.24 % 86.13 % 76.38 %
Car (3D Detection) 22.75 % 17.13 % 15.63 %
Car (Bird's Eye View) 31.13 % 23.46 % 20.97 %
Pedestrian (Detection) 46.50 % 34.43 % 31.06 %
Pedestrian (Orientation) 28.43 % 20.54 % 18.36 %
Pedestrian (3D Detection) 13.20 % 8.26 % 7.02 %
Pedestrian (Bird's Eye View) 15.27 % 9.66 % 8.28 %
Cyclist (Detection) 45.35 % 29.89 % 26.49 %
Cyclist (Orientation) 30.64 % 20.13 % 18.19 %
Cyclist (3D Detection) 4.73 % 2.48 % 2.16 %
Cyclist (Bird's Eye View) 5.24 % 2.80 % 2.55 %
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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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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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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