Method

MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer [MonoDTR]


Submitted on 14 Nov. 2021 16:22 by
Kuan-Chih Huang (National Taiwan University)

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

Method Description:
MonoDTR
Parameters:
None
Latex Bibtex:
@inproceedings{huang2022monodtr,
title={MonoDTR: Monocular 3D Object Detection with
Depth-Aware Transformer},
author={Kuan-Chih Huang and Tsung-Han Wu and Hung-
Ting Su and Winston H. Hsu},
booktitle={CVPR},
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) 93.90 % 88.41 % 76.20 %
Car (Orientation) 93.12 % 86.70 % 74.53 %
Car (3D Detection) 21.99 % 15.39 % 12.73 %
Car (Bird's Eye View) 28.59 % 20.38 % 17.14 %
Pedestrian (Detection) 59.44 % 42.86 % 38.57 %
Pedestrian (Orientation) 49.41 % 35.11 % 31.41 %
Pedestrian (3D Detection) 15.33 % 10.18 % 8.61 %
Pedestrian (Bird's Eye View) 16.66 % 10.59 % 9.00 %
Cyclist (Detection) 64.93 % 49.48 % 42.76 %
Cyclist (Orientation) 56.40 % 42.45 % 36.32 %
Cyclist (3D Detection) 5.05 % 3.27 % 3.19 %
Cyclist (Bird's Eye View) 5.84 % 4.11 % 3.48 %
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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