Method

Hallucinated Hollow-3D R-CNN [H^23D R-CNN]
https://github.com/djiajunustc/H-23D_R-CNN

Submitted on 9 Jan. 2021 15:54 by
Jiajun Deng (University of Science and Technology of China)

Running time:0.03 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
TBD
Parameters:
TBD
Latex Bibtex:
@article{deng2021multi,
title={From Multi-View to Hollow-3D: Hallucinated
Hollow-3D R-CNN for 3D Object Detection},
author={Deng, Jiajun and Zhou, Wengang and Zhang,
Yanyong and Li, Houqiang},
journal={IEEE Transactions on Circuits and Systems
for Video Technology},
year={2021},
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) 96.20 % 93.20 % 90.55 %
Car (Orientation) 96.13 % 93.03 % 90.33 %
Car (3D Detection) 90.43 % 81.55 % 77.22 %
Car (Bird's Eye View) 92.85 % 88.87 % 86.07 %
Cyclist (Detection) 85.50 % 72.73 % 65.81 %
Cyclist (Orientation) 85.09 % 72.20 % 65.25 %
Cyclist (3D Detection) 78.67 % 62.74 % 55.78 %
Cyclist (Bird's Eye View) 82.76 % 67.90 % 60.49 %
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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