Method

Monocular 3D Object Detection with Depth Foundation Models [MonoDF]
https://github.com/lixiaole2016/MonoDF-OPEN

Submitted on 25 May. 2026 03:57 by
Shichao Gong (Southwest Jiaotong University)

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

Method Description:
A novel framework that leverages depth foundation
models for monocular 3D object detection.
Parameters:
batch_size=16
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) 89.50 % 83.45 % 76.22 %
Car (Orientation) 89.32 % 82.98 % 75.57 %
Car (3D Detection) 28.33 % 19.97 % 16.97 %
Car (Bird's Eye View) 37.17 % 26.86 % 23.43 %
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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