Method

P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds [P2V-RCNN]


Submitted on 22 Apr. 2021 16:30 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:
@article{P2V-RCNN,
author = {Jiale Li and
Shujie Luo and
Ziqi Zhu and
Hang Dai and
Andrey S. Krylov and
Yong Ding and
Ling Shao},
title = {P2V-RCNN: Point to Voxel Feature
Learning for 3D Object Detection from Point
Clouds},
journal = {IEEE Access},
volume = {},
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
Car (Detection) 96.03 % 94.73 % 92.34 %
Car (Orientation) 96.01 % 94.59 % 92.13 %
Car (3D Detection) 88.34 % 81.45 % 77.20 %
Car (Bird's Eye View) 92.72 % 88.63 % 86.14 %
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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