Method

Boundary-Aware 3D Object Detection from Point Clouds [BANet]
https://github.com/rui-qian/BANet

Submitted on 27 Feb. 2021 07:34 by
Hai Li (Tsinghua University)

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

Method Description:
arXiv: https://arxiv.org/pdf/2104.10330.pdf
Parameters:
TBD
Latex Bibtex:
@misc{qian2021boundaryaware,
title={Boundary-Aware 3D Object Detection from
Point Clouds},
author={Rui Qian and Xin Lai and Xirong Li},
year={2021},
eprint={2104.10330},
archivePrefix={arXiv},
primaryClass={cs.CV}
}

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) 98.75 % 95.61 % 90.64 %
Car (Orientation) 98.65 % 95.34 % 90.28 %
Car (3D Detection) 89.28 % 81.61 % 76.58 %
Car (Bird's Eye View) 95.23 % 91.32 % 86.48 %
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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