Method

MonoCLUE: Object-aware Clustering Enhances Monocular 3D Object Detectio [MonoCLUE]


Submitted on 28 Jul. 2025 05:58 by
sunghun yang (yonsei university)

Running time:0.05 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
Exploiting an unified local clustering and
generalized scene memory strategy, MonoCLUE enables
robust monocular 3D detection under occlusion and
limited visibility.
Parameters:
44.17M
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) 95.82 % 90.48 % 80.71 %
Car (Orientation) 95.54 % 89.76 % 79.86 %
Car (3D Detection) 26.30 % 18.77 % 16.10 %
Car (Bird's Eye View) 34.68 % 24.20 % 21.08 %
Pedestrian (Detection) 68.39 % 50.79 % 46.06 %
Pedestrian (Orientation) 60.20 % 44.00 % 39.60 %
Pedestrian (3D Detection) 16.03 % 10.32 % 8.76 %
Pedestrian (Bird's Eye View) 16.60 % 10.81 % 9.12 %
Cyclist (Detection) 60.57 % 40.16 % 34.45 %
Cyclist (Orientation) 47.86 % 31.74 % 27.18 %
Cyclist (3D Detection) 5.58 % 3.50 % 3.07 %
Cyclist (Bird's Eye View) 7.15 % 4.43 % 4.15 %
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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