Method

Multivariate Probabilistic Monocular 3D Object Detection [MonoRCNN++]
https://github.com/Rock-100/MonoDet

Submitted on 27 Aug. 2022 15:53 by
Jack (Imperial College London)

Running time:0.07 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
TBD
Parameters:
TBD
Latex Bibtex:
@inproceedings{MonoRCNN_WACV23,
title = {Multivariate Probabilistic Monocular 3D
Object Detection},
author = {Xuepeng Shi and Zhixiang Chen and Tae-Kyun
Kim},
booktitle = {WACV},
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) 94.31 % 86.69 % 71.87 %
Car (Orientation) 94.22 % 86.37 % 71.52 %
Car (3D Detection) 20.08 % 13.72 % 11.34 %
Car (Bird's Eye View) 27.20 % 18.62 % 15.69 %
Pedestrian (Detection) 60.29 % 45.76 % 39.39 %
Pedestrian (Orientation) 56.59 % 42.54 % 36.64 %
Pedestrian (3D Detection) 12.26 % 7.90 % 6.62 %
Pedestrian (Bird's Eye View) 13.45 % 9.04 % 7.74 %
Cyclist (Detection) 67.78 % 48.84 % 42.44 %
Cyclist (Orientation) 56.32 % 39.84 % 34.82 %
Cyclist (3D Detection) 3.17 % 1.81 % 1.75 %
Cyclist (Bird's Eye View) 3.50 % 2.31 % 2.01 %
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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