Method

Local Contextual Depth Integration for Monocular 3D Object Detection [MonoLCD]
[Anonymous Submission]

Submitted on 9 Sep. 2021 15:00 by
[Anonymous Submission]

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

Method Description:
We extend this with a feature fusion mechanism and
apply joint 3D scaling data augmentation to get
MonoLCD.
Parameters:
lr= 2.5*10-4
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) 93.74 % 88.33 % 78.59 %
Car (Orientation) 93.62 % 87.86 % 78.09 %
Car (3D Detection) 18.08 % 13.52 % 11.58 %
Car (Bird's Eye View) 25.89 % 18.68 % 16.30 %
Pedestrian (Detection) 57.69 % 43.71 % 39.60 %
Pedestrian (Orientation) 51.34 % 38.32 % 34.60 %
Pedestrian (3D Detection) 11.21 % 7.62 % 6.47 %
Pedestrian (Bird's Eye View) 12.73 % 8.89 % 7.72 %
Cyclist (Detection) 65.17 % 44.57 % 39.96 %
Cyclist (Orientation) 55.75 % 37.07 % 33.16 %
Cyclist (3D Detection) 4.46 % 2.75 % 2.64 %
Cyclist (Bird's Eye View) 5.27 % 3.33 % 2.90 %
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.
This figure as: png eps pdf txt gnuplot



3D object detection results.
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Bird's eye view results.
This figure as: png eps pdf txt gnuplot




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