Method

VeloFCN [la] [VeloFCN]


Submitted on 24 Jul. 2017 13:21 by
David Stutz (Max Planck Institute for Intelligent Systems)

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

Method Description:
Densebox on Velodyne scan.

Originally submitted by Bo Li
(http://prclibo.github.io/) and 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 (Bird's Eye View) 0.15 % 0.33 % 0.47 %
This table as LaTeX


Bird's eye view results.
This figure as: png eps pdf txt gnuplot




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