Method

3D Semantic Segmentation-Driven Representations for Precise 3D Object Detection [SeSame-point]
[Anonymous Submission]

Submitted on 31 Jul. 2024 07:00 by
[Anonymous Submission]

Running time:N/A s
Environment:TITAN RTX @ 1.35 Ghz (Python)

Method Description:
[la]
Parameters:
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) 95.22 % 93.50 % 90.44 %
Car (Orientation) 95.20 % 93.32 % 90.14 %
Car (3D Detection) 85.25 % 76.83 % 71.60 %
Car (Bird's Eye View) 90.84 % 87.49 % 83.77 %
Pedestrian (Detection) 60.29 % 51.27 % 49.06 %
Pedestrian (Orientation) 56.55 % 47.09 % 44.58 %
Pedestrian (3D Detection) 42.29 % 35.34 % 33.02 %
Pedestrian (Bird's Eye View) 48.25 % 41.22 % 39.18 %
Cyclist (Detection) 84.16 % 72.25 % 65.39 %
Cyclist (Orientation) 83.97 % 71.88 % 65.00 %
Cyclist (3D Detection) 69.55 % 54.56 % 48.34 %
Cyclist (Bird's Eye View) 75.73 % 61.70 % 55.27 %
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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