Bug#703579: ITP: PCL (Point Cloud Library) -- Framework for 3D point clouds data processing

[Paride Legovini <pl@ninthfloor.org>]

On Fri, Mar 22, 2013 at 12:49:11AM +0800, Thomas Goirand wrote:
> On 03/21/2013 09:51 AM, Paride Legovini wrote:
> > Package: wnpp
> > Severity: wishlist
> > Owner: Paride Legovini <pl@ninthfloor.org>
> >
> > * Package name    : PCL (Point Cloud Library)
> >   Version         : 1.6
> >   Upstream Author : Open Perception, Inc. (http://www.openperception.org/)
> > * URL             : http://www.pointclouds.org/
> > * License         : BSD-3-clause
> >   Programming Lang: C++
> >   Description     : Framework for 3D point clouds data processing
> >
> > PCL (Point Cloud Library) is a standalone open-source framework including
> > numerous state-of-the art algorithms for n-dimensional point clouds and
> > 3D geometry processing. The library contains algorithms for filtering,
> > feature estimation, surface reconstruction, registration, model fitting,
> > and segmentation. PCL is developed by a large consortium of researchers
> > and engineers around the world. It is written in C++ and released under
> > the BSD license.
> Hi,
> 
> By reading the above, I was tempted to believe that this was
> some kind of weather research tool. If this package reaches
> Debian, please make sure to come with a better long description.

Dear Thomas,

thanks for your suggestions. I will not change the term "point cloud",
as it is the correct technical term for what this library is for (see:
https://en.wikipedia.org/wiki/Point_cloud ), but I'll try to make the
description less ambiguous anyway. Maybe something the following would
be better:

 PCL (Point Cloud Library) is a standalone open-source framework
 including numerous state-of-the art algorithms for n-dimensional point
 clouds (sets of vertices in an n-dimensional coordinate system)
 and 3D geometry processing. The library contains algorithms for
 filtering, feature estimation, surface reconstruction, registration,
 model fitting, and segmentation.
 .
 These algorithms can be used, for example, for perception in robotics
 to filter outliers from noisy data, stitch 3D point clouds together,
 segment relevant parts of a scene, extract keypoints and compute
 descriptors to recognize objects in the world based on their geometric
 appearance, and create surfaces from point clouds and visualize them.

Let me know if you have further comments.

Cheers,

Paride