Method

Boosting Regression with Multiple Anchor Offsets for Monocular 3D Object Detection [MAOLoss]
[Anonymous Submission]

Submitted on 7 Sep. 2021 10:19 by
[Anonymous Submission]

Running time:0.05 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
We propose Multiple Anchor Offsets for learning
the offsets of multiple anchors simultaneously
instead of a single anchor which is robust to the
classification error
Parameters:
TBD
Latex Bibtex:
TBD

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) 89.31 % 73.79 % 63.59 %
Car (Orientation) 89.22 % 73.51 % 63.24 %
Car (3D Detection) 20.05 % 14.00 % 11.81 %
Car (Bird's Eye View) 28.29 % 19.95 % 16.94 %
Pedestrian (Detection) 32.07 % 23.60 % 21.55 %
Pedestrian (Orientation) 27.99 % 20.21 % 18.31 %
Pedestrian (3D Detection) 5.81 % 4.18 % 3.67 %
Pedestrian (Bird's Eye View) 6.63 % 4.74 % 4.19 %
Cyclist (Detection) 34.37 % 22.58 % 20.49 %
Cyclist (Orientation) 27.93 % 18.03 % 16.35 %
Cyclist (3D Detection) 6.71 % 4.06 % 3.16 %
Cyclist (Bird's Eye View) 7.28 % 4.49 % 3.60 %
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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