Method

Voxel Self-attention and Center Point [VSAC]


Submitted on 29 Mar. 2024 03:55 by
Jie Cao (西华大学)

Running time:0.07 s
Environment:1 core @ 1.0 Ghz (Python)

Method Description:
Voxel Self-attention and Center Point
Parameters:
Voxel Self-attention and Center Point
Latex Bibtex:
Voxel Self-attention and Center Point

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) 96.18 % 90.68 % 87.93 %
Car (Orientation) 96.16 % 90.57 % 87.72 %
Car (3D Detection) 85.06 % 76.29 % 71.65 %
Car (Bird's Eye View) 91.98 % 86.22 % 81.50 %
Pedestrian (Detection) 60.72 % 48.22 % 45.55 %
Pedestrian (Orientation) 58.47 % 45.97 % 43.24 %
Pedestrian (3D Detection) 45.26 % 37.02 % 33.35 %
Pedestrian (Bird's Eye View) 49.91 % 40.37 % 36.64 %
Cyclist (Detection) 88.58 % 69.36 % 62.27 %
Cyclist (Orientation) 88.31 % 69.14 % 62.03 %
Cyclist (3D Detection) 75.54 % 56.99 % 50.90 %
Cyclist (Bird's Eye View) 78.55 % 60.23 % 53.91 %
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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