[pgpointcloud] RLE and SIGBITS heuristics
Oscar Martinez Rubi
o.martinezrubi at tudelft.nl
Mon Apr 20 02:58:57 PDT 2015
Whoa!
Thanks guys for all the material! I am now busy reading it all!
Remi: I had to read your mail a few times ;) Great slides (I actually
looked at all of them, very well done!) Very interesting topics you are
researching!
Howard: About the Greyhoud+s3, but what storage solution do you use, it
is not clear...mongodb? I mean where are the points stored? file-based,
dbms?
Paul+Nouri: The geohash tree that Noury mentions is the ght compression
in pgpointclouid, right? I tried it once but there was some limitation
with the type of coordinates, they had to be long and lat so I guess
there need to be a reference system transformation in between, right?
Any place where I can find an example on how to use this?
At NLeSC for the visualization of data we are using a system based on
the potree visualization (so, file-based) but I am very very interested
on the stuff you are guys doing and I would love to be convinced that
DBMS solutions can be really efficient for visualization as well (i
think it is close now!). We choose file-based and potree because of the
initial lack of LoD support in DBMS, the speed the file-based approach
and the super compressed LAZ storage.
To see what we have done so far:
https://github.com/NLeSC/PattyVis
https://www.esciencecenter.nl/project/mapping-the-via-appia-in-3d
(see the video from 1:40 for the potree based visualization)
One of the many reasons I would loved to be convinced that DBMS is that
now we are considering how to visualize the 640B AHN2 dataset, and in a
pure file-based solution (like the potree) I fear that when
restructuring the data to octree we would need a number of octree
nodes/files probably larger than what ext4 can handle!. We will try, I
let you know how that goes ;), but it would be really nice to have a
efficient and fast DBMS-based alternative!
I am very happy though with all the different work you are all doing and
excited to see how fast things improve and evolve!!
Keep on like this guys!
Regards,
O.
On 17-04-15 19:01, Sabo, Nouri wrote:
>
> Hi,
>
> Thank you for sharing these ideas. Many of the ideas can make
> improvements. In the prototype we have developed at RNCan and that we
> mentioned in the paper in attachment we have implemented some of these
> concepts. For example, in the prototype we are sorting points
> according to the Morton pattern before creating blocks. And each block
> is composed only of points that are spatially close, thereby improving
> the level of compression. We also use the properties of the Morton
> curve (Z pattern) to do spatial queries using Geohash as BBox.
> Usually, in Geohash based system the more the Geohash prefixes for two
> points resemble one another, the more they are spatially close to each
> other. Unfortunately, this property is not always complied with two
> points located on either side of a subdivision line. For this reason
> we implemented a neighbourhood based strategy to allow spatial query
> based on the hash string.
>
> Also to improve the compression and performance we can change the
> encoding of Geohash. Currently, the hashes are encoded as base 32
> strings, which causes a lot of overhead (5 bits are inflated in 8 bits
> of character). Unfortunately, the current libght does not include all
> the concepts of GeoHashTree.
>
> Oscar, I will read your paper and get you back so we could continue to
> exchange.
>
> Kind regards!
>
> Nouri,
>
> *From:*Paul Ramsey [mailto:pramsey at cleverelephant.ca]
> *Sent:* 17 avril 2015 06:56
> *To:* pgpointcloud at lists.osgeo.org; Peter van Oosterom; Oscar Martinez
> Rubi; Howard Butler; Rémi Cura
> *Cc:* Sabo, Nouri
> *Subject:* Re: [pgpointcloud] RLE and SIGBITS heuristics
>
> Hi Oscar,
>
> This sounds like a slightly more sophisticated version of the work
> done at Natural Resources Canada for what they call “geohash tree”.
> They did find that they got pretty good compression (even with the
> simple ascii-based key!) using the scheme, and it did allow easy
> random access to subsets of the data.
>
> http://2013.foss4g.org/conf/programme/presentations/60/
>
> The downside was of course the cost of sorting things in the first
> place, but for a one-time cost on frequently accessed data, it’s not a
> bad thing. The “libght” soft dependency in pgpointcloud is to a (not
> so great) implementation of the scheme that I did for them a couple
> years ago. As a scheme, I think it cuts against the idea of having
> small patches that is core to the pgpointcloud concept. It makes more
> and more sense the larger your file is, in that it gets greater and
> greater leverage for random access.
>
> ATB,
>
> P.
>
> --
> Paul Ramsey
> http://cleverelephant.ca
>
> http://postgis.net
>
> On April 17, 2015 at 11:02:47 AM, Oscar Martinez Rubi
> (o.martinezrubi at tudelft.nl <mailto:o.martinezrubi at tudelft.nl>) wrote:
>
> Hi,
>
> About the XYZ binding for better compression. In our research in
> the NL escience center and TU Delft we have been thinking (not
> testing yet though) about one possible approach for this.
>
> It is based on using space filling curves. So, once you have the
> points that go in a block you could compute the morton/hilbert
> code of the XYZ. Since all the points are close together such
> codes will be extremely similar, so one could store only the
> increments which could fit in many few bits. We have not tested or
> compared this with any of the other compressions but we just
> wanted to share it with you just in case you find it useful!
>
> An additional improvement would be to sort the points within the
> blocks according to the morton code. Then, when doing crop/filter
> operations in the blocks one can use the morton codes for the
> queries similarly to what we presented in our papers with the flat
> table (without blocks), I attach one of them (see section 5.2). In
> a nutshell: You convert the query region into a set of
> quadtree/octree nodes which can be also converted to morton code
> ranges (thanks to relation between morton/hilbert curve and a
> quadtree/octree). You scale down the ranges to increments (like
> you did when storing the point of the block) and then you simply
> do range queries in sorted data with a binary algorithm. In this
> way you avoid the decompression of the morton code for most of the
> block. This filtering is equivalent to a bbox filter so it still
> requires a point in polygon check for some of the points.
>
> Kind Regards,
>
> Oscar.
>
> On 16-04-15 18:15, Rémi Cura wrote:
>
> epic fail ! I had avoided html just for you
>
>
> Dataset |subset size | compressing | decompressing |
> |(Million pts)|(Million pts/s)|(Million pts/s)|
> Lidar | 473.3 | 4,49 | 4,67 |
>
> 21-atributes | 105.7 | 1,11 | 2,62 |
>
> Stereo | 70 | 2,44 | 7,38 |
>
> Cheers
>
> 2015-04-16 17:42 GMT+02:00 Sandro Santilli <strk at keybit.net
> <mailto:strk at keybit.net>>:
>
> On Thu, Apr 16, 2015 at 05:30:12PM +0200, Rémi Cura wrote:
> > OUps
> >
> > Dataset | subset size(Million pts) | compressing
> (Million pts/s) |
> > decompressing (Million pts/s)
> > Lidar | 473.3 | 4,49
> > | __4,67__
> > 21 attributes | 105.7 |
> > 1,11 | 2,62
> > Stereo | 70 | 2,44
> > | 7,38
>
> These tables aren't really readable here.
> Could you make sure to use a fixed-width font to write those
> tables
> and to keep lines within 70 columns at most ?
>
> --strk;
>
>
>
>
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