Method

3D FCN[la] [3D FCN]
[Anonymous Submission]

Submitted on 10 Aug. 2016 07:03 by
[Anonymous Submission]

Running time:>5 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
3DCNN
Parameters:
0-6000
Latex Bibtex:
@inproceedings{li2017iros,
title = {3D Fully Convolutional Network for Vehicle Detection
in Point Cloud},
author = {Bo Li},
booktitle = {IROS},
year = {2017}
}

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) 85.54 % 75.83 % 68.30 %
Car (Orientation) 85.46 % 75.71 % 68.19 %
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