Method

MonoDGC [MonoDGC]
https://github.com/Tracygc/MonoDGC

Submitted on 27 Jan. 2026 18:25 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: Image-Plane Dynamic Graph Cross-Former for
Monocular 3D Object Detection
Parameters:
K=9
Latex Bibtex:
@article {guo2026monodgc,
author = {Guo, Cong and Guo, Ling and Lin,
Wenhao and Ma, Yuzhuo and Ren, Kan and Chen,
Qian},
title = {MonoDGC: Image-Plane Dynamic Graph
Cross-Former for Monocular 3D Object Detection},
note = {Manuscript submitted to IEEE
Transactions on Image Processing and currently
under peer review},
year = {2026},
institution = {Jiangsu Key Laboratory of Visual
Sensing \& Intelligent Perception},
url = {https://github.com/Tracygc/MonoDGC}
}

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) 94.34 % 89.22 % 79.68 %
Car (Orientation) 94.10 % 88.62 % 78.98 %
Car (3D Detection) 27.13 % 19.42 % 16.64 %
Car (Bird's Eye View) 35.18 % 24.71 % 22.38 %
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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