Method

Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud [MGAF-3DSSD]
https://github.com/jialeli1/MGAF-3DSSD

Submitted on 26 Jan. 2021 04:31 by
Jiale Li (Zhejiang University)

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

Method Description:
See paper
Parameters:
See paper
Latex Bibtex:
@inproceedings{Anchor-free-MGA-SSD,
author = {Jiale Li and
Hang Dai and
Ling Shao and
Yong Ding},
title = {Anchor-free 3D Single Stage
Detector with Mask-Guided Attention for Point
Cloud},
booktitle = {{MM} '21: The 29th {ACM}
International Conference on Multimedia (ACM MM)},
publisher = {{ACM}},
year = {2021},
}

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
Pedestrian (Detection) 66.31 % 55.80 % 52.02 %
Pedestrian (Orientation) 64.69 % 53.73 % 49.84 %
Pedestrian (3D Detection) 50.65 % 43.09 % 39.65 %
Pedestrian (Bird's Eye View) 56.09 % 48.46 % 44.90 %
Cyclist (Detection) 86.42 % 70.41 % 63.26 %
Cyclist (Orientation) 86.28 % 70.16 % 62.99 %
Cyclist (3D Detection) 80.64 % 63.43 % 55.15 %
Cyclist (Bird's Eye View) 83.03 % 66.00 % 57.57 %
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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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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