Method

3D FCN[la] [3D FCN]


Submitted on 10 Aug. 2016 07:03 by
Bo Li ()

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) 86.74 % 74.65 % 67.85 %
Car (Orientation) 86.65 % 74.54 % 67.73 %
This table as LaTeX

2D object detection results.
This figure as: png eps pdf txt gnuplot



Orientation estimation results.
This figure as: png eps pdf txt gnuplot




eXTReMe Tracker