Method

Bi-contextual attention module [BCA]
[Anonymous Submission]

Submitted on 16 Nov. 2022 06:01 by
[Anonymous Submission]

Running time:0.17 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
We proposed a plugged-in-plug module for monocular
3D object detection. The proposed method utilizes
bi-contextual attention (BCA) for estimating
object' translation. BCA faithfully predicts
translation by exploiting relation-context inter
objects and scene-contest between an object and
rad environment.
Parameters:
detection thr = 0.2
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) 94.38 % 86.62 % 76.67 %
Car (Orientation) 93.93 % 85.95 % 75.82 %
Car (3D Detection) 21.82 % 14.54 % 11.99 %
Car (Bird's Eye View) 30.03 % 20.75 % 17.60 %
Pedestrian (Detection) 74.33 % 55.20 % 50.26 %
Pedestrian (Orientation) 68.73 % 50.60 % 45.83 %
Pedestrian (3D Detection) 13.60 % 8.85 % 8.05 %
Pedestrian (Bird's Eye View) 15.00 % 9.99 % 8.49 %
Cyclist (Detection) 67.68 % 46.50 % 39.47 %
Cyclist (Orientation) 57.81 % 38.76 % 32.79 %
Cyclist (3D Detection) 5.89 % 3.54 % 3.34 %
Cyclist (Bird's Eye View) 6.89 % 4.42 % 3.91 %
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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