Method

Voxel query and Pyramid Network [VPN]


Submitted on 18 Oct. 2021 05:42 by
sheng yang (Beijing University of Aeronautics and Astronautics, China)

Running time:0.06 s
Environment:1 core @ 2.5 Ghz (Python + C/C++)

Method Description:
VFN uses the structure of voxel query and pyramid to
enhance pedestrian features in point cloud
information, with faster detection time and
better detection performance
Parameters:
epochs=80 batch size=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) 96.19 % 93.30 % 88.30 %
Car (Orientation) 96.16 % 93.08 % 88.01 %
Car (3D Detection) 85.02 % 77.93 % 72.97 %
Car (Bird's Eye View) 90.94 % 88.06 % 83.24 %
Pedestrian (Detection) 70.97 % 59.48 % 55.29 %
Pedestrian (Orientation) 66.91 % 55.05 % 50.92 %
Pedestrian (3D Detection) 54.13 % 44.56 % 41.73 %
Pedestrian (Bird's Eye View) 57.98 % 49.19 % 45.26 %
Cyclist (Detection) 89.56 % 73.99 % 66.86 %
Cyclist (Orientation) 89.25 % 73.66 % 66.54 %
Cyclist (3D Detection) 77.81 % 61.82 % 55.33 %
Cyclist (Bird's Eye View) 82.20 % 65.60 % 58.96 %
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.
This figure as: png eps pdf txt gnuplot



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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