Method

Objects are Different: Flexible Monocular 3D Object Detection [MonoFlex]
[Anonymous Submission]

Submitted on 16 Nov. 2020 10:31 by
[Anonymous Submission]

Running time:0.03 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
We propose a flexible framework for monocular 3D
object detection which considers the difference
among objects and design an adaptive ensemble
method for object depth inference.
Parameters:
lr = 3e-4, weight-decay=1e-5, 34k iterations.
Latex Bibtex:

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) 96.01 % 91.02 % 83.38 %
Car (Orientation) 95.95 % 90.82 % 83.11 %
Car (3D Detection) 19.94 % 13.89 % 12.07 %
Car (Bird's Eye View) 28.23 % 19.75 % 16.89 %
Pedestrian (Detection) 62.64 % 47.58 % 43.15 %
Pedestrian (Orientation) 58.96 % 44.20 % 39.89 %
Pedestrian (3D Detection) 9.43 % 6.31 % 5.26 %
Pedestrian (Bird's Eye View) 10.36 % 7.36 % 6.29 %
Cyclist (Detection) 72.41 % 54.76 % 46.21 %
Cyclist (Orientation) 65.51 % 47.91 % 40.40 %
Cyclist (3D Detection) 4.17 % 2.35 % 2.04 %
Cyclist (Bird's Eye View) 4.41 % 2.67 % 2.50 %
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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