Method

FADNet [FADNet]
n/a

Submitted on 26 Nov. 2020 13:39 by
Tianze Gao (Harbin Institute of Technology)

Running time:0.04 s
Environment:GPU @ >3.5 Ghz (Python)

Method Description:
Monocular 3D Object Detection with Sequential Feature Association and Depth Hint Augmentation
Parameters:
n/a
Latex Bibtex:
n/a

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) 96.15 % 90.49 % 80.71 %
Car (Orientation) 95.89 % 89.84 % 79.98 %
Car (3D Detection) 16.37 % 9.92 % 8.05 %
Car (Bird's Eye View) 23.00 % 14.22 % 12.56 %
Pedestrian (Detection) 42.43 % 32.64 % 29.13 %
Pedestrian (Orientation) 33.68 % 25.41 % 22.66 %
Pedestrian (3D Detection) 5.40 % 3.53 % 3.31 %
Pedestrian (Bird's Eye View) 6.46 % 4.45 % 3.70 %
Cyclist (Detection) 59.77 % 43.40 % 37.28 %
Cyclist (Orientation) 43.53 % 30.78 % 26.29 %
Cyclist (3D Detection) 1.44 % 0.64 % 0.67 %
Cyclist (Bird's Eye View) 1.54 % 0.94 % 0.79 %
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.
This figure as: png eps pdf txt gnuplot



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




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