Method

LGNet-Car [LGNet-Car]
[Anonymous Submission]

Submitted on 20 Jan. 2024 13:34 by
[Anonymous Submission]

Running time:0.11 s
Environment:1 core @ 2.5 Ghz (Python + C/C++)

Method Description:
Local and Global Point Dependency Network for 3D
Object Detection
Parameters:
None
Latex Bibtex:
None

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) 96.52 % 95.43 % 92.73 %
Car (Orientation) 96.51 % 95.31 % 92.55 %
Car (3D Detection) 90.65 % 82.02 % 77.34 %
Car (Bird's Eye View) 92.83 % 88.98 % 86.26 %
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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