Method

Monodgc-3classes [Monodgc-3classes]
https://github.com/Tracygc/MonoDGC

Submitted on 27 Jan. 2026 19:20 by
Cong Guo (Nanjing University of Science and Technology)

Running time:0.03 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
MonoDGC: Dynamic Graph Cross-Former for Monocular 3D
Object Detection
Parameters:
K=9
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) 95.15 % 89.99 % 80.29 %
Car (Orientation) 94.85 % 89.23 % 79.43 %
Car (3D Detection) 26.43 % 18.62 % 15.89 %
Car (Bird's Eye View) 35.75 % 24.57 % 21.26 %
Pedestrian (Detection) 66.73 % 50.26 % 45.48 %
Pedestrian (Orientation) 59.88 % 44.25 % 39.77 %
Pedestrian (3D Detection) 16.14 % 10.50 % 8.73 %
Pedestrian (Bird's Eye View) 17.16 % 10.95 % 9.20 %
Cyclist (Detection) 60.23 % 39.70 % 35.10 %
Cyclist (Orientation) 49.51 % 32.33 % 28.48 %
Cyclist (3D Detection) 7.17 % 3.91 % 3.38 %
Cyclist (Bird's Eye View) 8.68 % 5.03 % 4.41 %
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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