Method

Behind the Curtain [la] [BtcDet]
https://arxiv.org/pdf/2112.02205.pdf

Submitted on 3 May. 2021 09:30 by
Qiangeng Xu (University of Southern California)

Running time:0.09 s
Environment:GPU @ 2.5 Ghz (Python + C/C++)

Method Description:
Learning Occ for 3D object detection
Parameters:
\range=5,9,5
Latex Bibtex:
@inproceedings{xu2020behind,
title={Behind the Curtain: Learning Occluded
Shapes for 3D Object Detection},
author={Xu, Qiangeng and Zhong, Yiqi and Neumann,
Ulrich},
booktitle={Proceedings of the AAAI Conference on
Artificial Intelligence},
volume={36},
year={2022}
}

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) 96.23 % 93.47 % 88.55 %
Car (Orientation) 39.26 % 38.00 % 36.82 %
Car (3D Detection) 90.64 % 82.86 % 78.09 %
Car (Bird's Eye View) 92.81 % 89.34 % 84.55 %
Cyclist (Detection) 88.41 % 80.46 % 74.59 %
Cyclist (Orientation) 35.79 % 33.94 % 31.90 %
Cyclist (3D Detection) 82.81 % 68.68 % 61.81 %
Cyclist (Bird's Eye View) 84.48 % 71.76 % 64.70 %
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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