Method

pointpillars-unofficial [PP-3D]


Submitted on 24 Aug. 2020 12:29 by
aihan liu (The University of Auckland)

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

Method Description:
pointpillars-unofficial model
Parameters:
alpha=0.2
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) 96.58 % 93.50 % 88.35 %
Car (Orientation) 38.66 % 37.20 % 36.29 %
Car (3D Detection) 88.33 % 79.47 % 72.29 %
Car (Bird's Eye View) 93.11 % 89.17 % 83.90 %
Pedestrian (Detection) 71.59 % 58.20 % 54.06 %
Pedestrian (Orientation) 39.16 % 31.86 % 29.65 %
Pedestrian (3D Detection) 51.92 % 43.77 % 40.14 %
Pedestrian (Bird's Eye View) 55.36 % 47.07 % 44.61 %
Cyclist (Detection) 85.75 % 75.08 % 68.69 %
Cyclist (Orientation) 36.29 % 32.37 % 29.81 %
Cyclist (3D Detection) 78.60 % 63.48 % 57.08 %
Cyclist (Bird's Eye View) 81.17 % 67.28 % 59.67 %
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.
This figure as: png eps pdf txt gnuplot



2D object detection results.
This figure as: png eps pdf txt gnuplot



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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