Method

Frustum-based PointNets for 3D Object Detection [la] [F-PointNet]
[Anonymous Submission]

Submitted on 15 Nov. 2017 01:28 by
[Anonymous Submission]

Running time:0.17 s
Environment:GPU @ 3.0 Ghz (Python)

Method Description:
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Parameters:
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Latex Bibtex:
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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) 90.78 % 90.00 % 80.80 %
Car (3D Detection) 81.20 % 70.39 % 62.19 %
Car (Bird's Eye View) 88.70 % 84.00 % 75.33 %
Pedestrian (Detection) 87.81 % 77.25 % 74.46 %
Pedestrian (3D Detection) 51.21 % 44.89 % 40.23 %
Pedestrian (Bird's Eye View) 58.09 % 50.22 % 47.20 %
Cyclist (Detection) 84.90 % 72.25 % 65.14 %
Cyclist (3D Detection) 71.96 % 56.77 % 50.39 %
Cyclist (Bird's Eye View) 75.38 % 61.96 % 54.68 %
This table as LaTeX


2D object detection 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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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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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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