Method

Color intuitive feature guided depth-height fusion and volume rendering for monocular 3D detection [MonoFRD]
[Anonymous Submission]

Submitted on 27 Mar. 2024 02:46 by
[Anonymous Submission]

Running time:0.01 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
A monocular 3d object detection method.
Parameters:
TBD
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) 95.25 % 87.31 % 77.66 %
Car (Orientation) 95.01 % 86.58 % 76.82 %
Car (3D Detection) 21.11 % 16.24 % 14.97 %
Car (Bird's Eye View) 29.65 % 22.77 % 20.41 %
Pedestrian (Detection) 54.06 % 41.20 % 37.53 %
Pedestrian (Orientation) 33.47 % 24.92 % 22.38 %
Pedestrian (3D Detection) 13.86 % 8.88 % 7.53 %
Pedestrian (Bird's Eye View) 15.68 % 10.38 % 8.79 %
Cyclist (Detection) 55.86 % 38.98 % 34.32 %
Cyclist (Orientation) 39.05 % 26.87 % 24.09 %
Cyclist (3D Detection) 6.38 % 3.33 % 3.12 %
Cyclist (Bird's Eye View) 8.44 % 4.55 % 4.14 %
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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