Method

MonoLTKD: Learning-Tailored Knowledge Distillation framework for Monocular 3D Object Detectio [MonoLTKD_V3]


Submitted on 3 Dec. 2023 03:20 by
YUEHCHENG HUANG (University of Washington )

Running time:0.04 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
Monocular 3D object detection (Mono3D) is an
indispensable research topic in autonomous driving
thanks to the cost-effective monocular camera
sensors and wide range of applications. Since the
image perspective has depth ambiguity, the
challenges of Mono3D lie in acquiring a semantic
understanding of 3D scenes and reconstructing 3D
object
007 information from a single image.
Parameters:
alpha=0.2
Latex Bibtex:
\inproceedings

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) 93.84 % 83.31 % 77.95 %
Car (Orientation) 93.72 % 82.72 % 77.10 %
Car (3D Detection) 27.91 % 19.42 % 16.51 %
Car (Bird's Eye View) 38.75 % 27.75 % 24.13 %
Pedestrian (Detection) 54.33 % 38.60 % 34.12 %
Pedestrian (Orientation) 35.52 % 24.37 % 21.37 %
Pedestrian (3D Detection) 16.15 % 10.41 % 9.68 %
Pedestrian (Bird's Eye View) 19.79 % 13.62 % 11.92 %
Cyclist (Detection) 58.63 % 38.84 % 33.99 %
Cyclist (Orientation) 37.61 % 24.18 % 21.44 %
Cyclist (3D Detection) 13.54 % 7.23 % 6.86 %
Cyclist (Bird's Eye View) 16.90 % 9.42 % 8.29 %
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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