Method

3D Object Detection with Monocular Images [3D-Mono]
[Anonymous Submission]

Submitted on 6 Nov. 2017 06:38 by
[Anonymous Submission]

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (Python + C/C++)

Method Description:
3D object detection by extending Faster R-CNN with only
monocular images.
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) 90.27 % 85.25 % 70.69 %
Car (Orientation) 90.09 % 84.52 % 70.00 %
Car (3D Detection) 6.25 % 4.64 % 4.19 %
Car (Bird's Eye View) 12.64 % 7.93 % 7.36 %
Pedestrian (Detection) 75.02 % 61.73 % 55.45 %
Pedestrian (Orientation) 68.01 % 55.51 % 49.77 %
Pedestrian (3D Detection) 0.69 % 0.69 % 0.65 %
Pedestrian (Bird's Eye View) 0.85 % 1.30 % 1.30 %
Cyclist (Detection) 65.32 % 50.84 % 45.27 %
Cyclist (Orientation) 52.31 % 40.68 % 36.85 %
Cyclist (3D Detection) 0.22 % 0.22 % 0.22 %
Cyclist (Bird's Eye View) 1.30 % 1.30 % 1.30 %
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.
This figure as: png eps pdf txt gnuplot




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