Method

VeloFCN[la] [VeloFCN]


Submitted on 10 Nov. 2016 06:40 by
Bo Li ()

Running time:1 s
Environment:GPU @ 2.5 Ghz (Python + C/C++)

Method Description:
Densebox on Velodyne scan,
2nd submission by Ji Wan.
Parameters:
Latex Bibtex:
@inproceedings{li,
author = {Bo Li and Tianlei Zhang and Tian Xia},
title = {Vehicle Detection from 3D Lidar Using Fully Convolutional Network},
booktitle = {RSS 2016}
}

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) 70.53 % 51.82 % 45.70 %
Car (Orientation) 70.03 % 51.05 % 44.82 %
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