Method

[la] Lidar Multiclass Net Version2 [LMNetV2]
[Anonymous Submission]

Submitted on 10 Nov. 2017 04:07 by
[Anonymous Submission]

Running time:0.02 s
Environment:GPU @ 2.5 Ghz (C/C++)

Method Description:
TBD
Parameters:
TBD
Latex Bibtex:
TBD

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) 59.58 % 44.20 % 37.90 %
Car (Orientation) 58.86 % 43.40 % 37.15 %
Car (3D Detection) 14.75 % 15.24 % 12.85 %
Car (Bird's Eye View) 42.80 % 32.17 % 32.85 %
Pedestrian (Detection) 27.03 % 23.00 % 23.26 %
Pedestrian (Orientation) 18.18 % 14.79 % 14.67 %
Pedestrian (3D Detection) 13.64 % 11.46 % 11.57 %
Pedestrian (Bird's Eye View) 18.47 % 15.00 % 14.71 %
Cyclist (Detection) 18.67 % 12.80 % 12.43 %
Cyclist (Orientation) 13.37 % 8.33 % 8.14 %
Cyclist (3D Detection) 2.84 % 3.23 % 3.28 %
Cyclist (Bird's Eye View) 6.57 % 4.87 % 5.05 %
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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