Method

sa-voxel-centernet-v2 [sa-voxel-centernet]
https://github.com/sunjingnjupt/sa-voxel-centernet

Submitted on 13 Jun. 2021 15:18 by
yang yong guang (NJUPT)

Running time:0.04 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
Semantic-aware 3D-voxel CenterNet for point cloud
object detection
Parameters:
TEB
Latex Bibtex:
TEB

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) 95.88 % 94.53 % 92.00 %
Car (Orientation) 95.86 % 94.39 % 91.80 %
Car (3D Detection) 87.39 % 80.77 % 76.45 %
Car (Bird's Eye View) 92.02 % 89.74 % 85.69 %
Pedestrian (Detection) 66.03 % 57.79 % 55.61 %
Pedestrian (Orientation) 62.48 % 53.44 % 51.00 %
Pedestrian (3D Detection) 46.08 % 40.24 % 38.07 %
Pedestrian (Bird's Eye View) 51.01 % 45.20 % 43.25 %
Cyclist (Detection) 88.08 % 79.98 % 73.61 %
Cyclist (Orientation) 87.91 % 79.21 % 72.82 %
Cyclist (3D Detection) 81.88 % 69.03 % 61.66 %
Cyclist (Bird's Eye View) 82.76 % 71.90 % 65.41 %
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.
This figure as: png eps pdf txt gnuplot




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