Method

FARP-Net: Local-Global Feature Aggregation and Relation-Aware Proposals for 3D Object Detection [FARP-Net]
https://github.com/XT-1997/FARP-Net

Submitted on 16 Nov. 2022 07:59 by
Tao Xie (Harbin Institute of Technology)

Running time:0.06 s
Environment:GPU @ 2.5 Ghz (Python + C/C++)

Method Description:
FARP-Net: Local-Global Feature Aggregation and
Relation-Aware Proposals for 3D Object Detection
Parameters:
n/a
Latex Bibtex:
@ARTICLE{10123008,
author={Xie, Tao and Wang, Li and Wang, Ke and
Li, Ruifeng and Zhang, Xinyu and Zhang, Haoming
and Yang, Linqi and Liu, Huaping and Li, Jun},
journal={IEEE Transactions on Multimedia},
title={FARP-Net: Local-Global Feature
Aggregation and Relation-Aware Proposals for 3D
Object Detection},
year={2023},
volume={},
number={},
pages={1-15},
doi={10.1109/TMM.2023.3275366}}

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) 96.11 % 95.57 % 93.07 %
Car (Orientation) 96.10 % 95.53 % 92.98 %
Car (3D Detection) 88.36 % 81.53 % 78.98 %
Car (Bird's Eye View) 91.20 % 88.45 % 86.01 %
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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