Method

MonoDTF [MonoDTF]
https://github.com/QiuDeZhang/MonoDTF

Submitted on 19 Oct. 2024 16:36 by
He Yinxi (Beijing Jiaotong University)

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
None
Parameters:
None
Latex Bibtex:
@ARTICLE{11364191,
author={Zhang, Qiude and Lin, Chunyu and Shen,
Zhijie and Nie, Lang and Zhao, Yao},
journal={IEEE Transactions on Circuits and
Systems for Video Technology},
title={Revisiting Monocular 3D Object Detection
with Depth Thickness Field},
year={2026},
volume={},
number={},
pages={1-1},
keywords={Three-dimensional
displays;Accuracy;Object detection;Feature
extraction;Depth
measurement;Encoding;Videos;Circuits and
systems;Cameras;Image coding;Monocular 3D object
detection;deep learning;depth
representation;autonomous driving},
doi={10.1109/TCSVT.2026.3658133}}

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) 95.03 % 91.35 % 85.92 %
Car (Orientation) 94.92 % 91.13 % 85.52 %
Car (3D Detection) 32.02 % 21.19 % 18.80 %
Car (Bird's Eye View) 42.67 % 28.92 % 25.89 %
This table as LaTeX

2D object detection results.
This figure as: png eps txt gnuplot



Orientation estimation results.
This figure as: png eps txt gnuplot



3D object detection results.
This figure as: png eps txt gnuplot



Bird's eye view results.
This figure as: png eps txt gnuplot




eXTReMe Tracker