Method

Monocular 3D object detection using Faster R-CNN and geometry constraints network [Shift R-CNN]
[Anonymous Submission]

Submitted on 30 Jan. 2019 16:08 by
[Anonymous Submission]

Running time:0.25 s
Environment:GPU @ 1.5 Ghz (Python)

Method Description:
Faster R-CNN dimension prediction and angle orientation regression using monocular images. PnP problem estimate solution used as input for another Shift network for further refining the 3D proposals.
Parameters:
Not applicable.
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) 90.56 % 88.90 % 79.86 %
Car (Orientation) 90.27 % 87.91 % 78.72 %
Car (3D Detection) 8.13 % 5.22 % 4.78 %
Car (Bird's Eye View) 13.32 % 8.49 % 6.40 %
Pedestrian (Detection) 71.11 % 53.33 % 44.71 %
Pedestrian (Orientation) 65.39 % 48.81 % 41.05 %
Pedestrian (3D Detection) 13.36 % 10.59 % 10.59 %
Pedestrian (Bird's Eye View) 13.81 % 11.44 % 10.76 %
Cyclist (Detection) 65.56 % 42.30 % 41.40 %
Cyclist (Orientation) 54.31 % 34.77 % 34.04 %
Cyclist (3D Detection) 3.03 % 3.03 % 3.03 %
Cyclist (Bird's Eye View) 3.58 % 3.03 % 3.03 %
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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