Method

MonoSample (DID-M3D) [MonoSample (DID-M3D)]
[Anonymous Submission]

Submitted on 6 Jan. 2024 14:22 by
[Anonymous Submission]

Running time:0.2 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
We have designed a data augmentation method for
monocular 3D object detection tasks, aimed at
enhancing the model's positional estimation
capabilities and alleviating overfitting due to
uncertainty loss. We experimented with this method
in DID-M3D and demonstrated in the validation set
that it can significantly improve model performance
Parameters:
\alpha=0.2
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) 96.45 % 95.02 % 85.58 %
Car (Orientation) 96.30 % 94.69 % 85.10 %
Car (3D Detection) 28.63 % 18.05 % 15.19 %
Car (Bird's Eye View) 37.64 % 23.94 % 20.46 %
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