Method

Point Assisted Sample Selection for PointPillar [PASS-PointPillar]


Submitted on 7 Aug. 2023 18:57 by
Haolin Zhang (Xi'an Jiaotong University)

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

Method Description:
Point Assisted Sample Selection for PointPillar
Parameters:
\tau = 5
Latex Bibtex:
@article{context,
title={Leveraging Anchor-based LiDAR 3D Object
Detection via Point Assisted Sample Selection},
author={Anonymous},
journal={will submit to computer vision conference/journal},
year={2024}
}

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) 95.20 % 92.09 % 88.73 %
Car (Orientation) 95.15 % 91.82 % 88.31 %
Car (3D Detection) 84.72 % 74.85 % 69.05 %
Car (Bird's Eye View) 91.07 % 87.23 % 81.98 %
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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