Method

PA3DNet: 3-D Vehicle Detection with Pseudo Shape Segmentation and Adaptive Camera-LiDAR Fusion [PA3DNet]


Submitted on 14 Mar. 2023 05:00 by
Lin Zhao (Beijing Institute of Technology)

Running time:0.1 s
Environment:GPU @ 2.5 Ghz (Python + C/C++)

Method Description:
Multimodal Fusion;
Parameters:
-
Latex Bibtex:
@ARTICLE{10034840,
author={Wang, Meiling and Zhao, Lin and Yue,
Yufeng},
journal={IEEE Transactions on Industrial
Informatics},
title={PA3DNet: 3-D Vehicle Detection with
Pseudo Shape Segmentation and Adaptive Camera-
LiDAR Fusion},
year={2023},
volume={},
number={},
pages={1-11},
doi={10.1109/TII.2023.3241585}}

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) 96.57 % 93.62 % 88.65 %
Car (Orientation) 96.56 % 93.55 % 88.56 %
Car (3D Detection) 90.49 % 82.57 % 77.88 %
Car (Bird's Eye View) 93.11 % 89.46 % 84.60 %
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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