Method

Leveraging Fractional Transform [LFT]
[Anonymous Submission]

Submitted on 7 Feb. 2024 12:22 by
[Anonymous Submission]

Running time:0.1s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
We have focused on studying the essential
characteristics of pseudo point clouds
Parameters:
\alpha = 0.1927
\direction = 45
\width = [0.1 0.1 0.1]
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) 99.29 % 96.27 % 88.94 %
Car (Orientation) 99.15 % 95.87 % 88.47 %
Car (3D Detection) 91.80 % 83.32 % 78.29 %
Car (Bird's Eye View) 95.83 % 90.12 % 85.06 %
This table as LaTeX

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



Orientation estimation results.
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3D object detection results.
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Bird's eye view results.
This figure as: png eps txt gnuplot




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