Method

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


Submitted on 30 Jul. 2025 07:44 by
lee wonjoon (Korea Aerospace University)

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

Method Description:
Monocular 3D object detection offers a cost-
effective solu-
tion for autonomous driving, but it suffers from
the ill-posed
depth and a limited field of view. These
constraints lead to the
lack of geometric cues and reduced accuracy in
occluded or
truncated scenes. While recent approaches
incorporate addi-
tional depth information to address geometric
ambiguity, they
overlook the importance of visual cues essential
for robust
object recognition
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) 95.56 % 90.38 % 80.58 %
Car (Orientation) 95.38 % 89.76 % 79.83 %
Car (3D Detection) 26.59 % 18.70 % 15.99 %
Car (Bird's Eye View) 35.83 % 24.59 % 21.36 %
Pedestrian (Detection) 65.74 % 49.35 % 44.70 %
Pedestrian (Orientation) 58.35 % 43.01 % 38.73 %
Pedestrian (3D Detection) 16.18 % 10.45 % 8.75 %
Pedestrian (Bird's Eye View) 17.67 % 11.21 % 9.35 %
Cyclist (Detection) 57.61 % 38.79 % 33.28 %
Cyclist (Orientation) 42.13 % 28.44 % 24.66 %
Cyclist (3D Detection) 5.93 % 3.20 % 2.94 %
Cyclist (Bird's Eye View) 7.48 % 4.04 % 3.43 %
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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