Method

Center-based 3d object detection and tracking [centerpoint_pcdet]


Submitted on 31 Mar. 2024 12:51 by
ma feifei (SEU)

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

Method Description:
Center-based 3d object detection and tracking
Parameters:
None
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) 94.73 % 92.94 % 89.50 %
Car (Orientation) 94.72 % 92.82 % 89.33 %
Car (3D Detection) 83.47 % 76.12 % 71.17 %
Car (Bird's Eye View) 90.04 % 87.04 % 83.32 %
Pedestrian (Detection) 65.95 % 57.06 % 55.08 %
Pedestrian (Orientation) 63.77 % 54.48 % 52.20 %
Pedestrian (3D Detection) 46.68 % 39.74 % 37.33 %
Pedestrian (Bird's Eye View) 51.41 % 45.22 % 43.05 %
Cyclist (Detection) 86.02 % 75.03 % 68.18 %
Cyclist (Orientation) 85.81 % 74.76 % 67.86 %
Cyclist (3D Detection) 76.27 % 59.80 % 53.02 %
Cyclist (Bird's Eye View) 79.83 % 64.99 % 58.43 %
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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