Messages

46

General / Re: Bringing a dense cloud back into photoscan

« on: December 02, 2017, 06:42:08 PM »

Thanks Alexey
I need the process to work as though the imported dense cloud is generated from the point cloud in photoscan, so there needs to be a "connection" (sorry, don't know the right words!) with the original photographs, to be able to texture the generated mesh.
Before I start to test, is there anything in particular to look out for?

47

General / Bringing a dense cloud back into photoscan

« on: December 02, 2017, 10:12:57 AM »

Hi,
The workflow I am using involves doing the alignment with Photoscan, then exporting as a bundler.out format, and using other software to generate the dense cloud and to clean up that dense cloud.  This provides me with a really nice clean dense cloud.
My question is, is there a way to bring that clean dense cloud back into Photoscan in order to mesh and texture in Photoscan?
Many thanks!

48

General / Re: Azure NC24 & Nvidia K80 not working with Photoscan (edit: DOES work)

« on: July 09, 2017, 02:58:05 PM »

Hi
Rather than starting a new thread on Azure GPU machines I hope you don't mind continuing on this one.
I've started to carry out some benchmarking on Azure VMs in anticipation of of a project where I form part of a team which will be using photogrammetry to document deep historic wrecks in the seas around Malta.  I am working with a sample set of 607 stills captured from HD video (so 2 Megapixel each still), attempting to compare the performance of the NV24 and NC24 VMs.  (More info on the VMs here https://azure.microsoft.com/en-us/blog/azure-n-series-preview-availability/ )
I aligned the images, optimised the cameras and generated the dense cloud.  In both cases I reset the Photoscan parameters to default to ensure the same baseline.  I ran the alignment and the dense cloud both on "High", otherwise leaving the settings as per default.
What has really confused me is that the different machines, running the same software version, with the same settings, on the same set of photos, generated different outcomes!  For starters, the NV alignment was more successful than the NC - after the initial run, on the NV just some 6-7 adjacent photos were not aligned and had to be aligned using the Right Click..Align method.  On the NC there was an additional set of some 15 adjacent photos which needed "forced" alignment.  The final outcome was the same though - 603 out of 607 aligned
When I ran the dense cloud, the NC was a lot faster, however on examining the result, the NC only generated some 7 million points vs the NV's 17 million - which I guess accounts at least in part for the NC being faster.
I am attaching the 2 different "info"s....there are a number of differences also in other areas (way above my head) for example the RMS reprojection errors are totally different, etc
Would be really curious to know if anyone can provide any explanation.
The models can be made available if anyone wants a closer look
Thanks,
John

49

General / Saving as high quality TIFF

« on: April 18, 2017, 10:48:56 PM »

Hi
I am using Photoscan Standard to document shipwrecks, and I would like to find a way of generating a high quality, large TIFF from the textured model.  There does not seem to be an easy way to do this (Export....TIFF), so I was wondering if anybody had any advice for me.
Thanks!
John

50

General / Azure NV VMs for Photoscan

« on: January 19, 2017, 11:03:13 PM »

Hi
Does anybody have experience using the Azure NV range of VMs for Photoscan?  The spec seems pretty impressive, as can be seen here
https://azure.microsoft.com/en-us/blog/azure-n-series-general-availability-on-december-1/
I have done a couple of quick tests (with the GPU enabled on a NV6 VM) and I expected a lightning performance compared to my i7 laptop with a Geforce 745M, but I was a little disappointed.  For example, the identical dense cloud generation took 22.3 secs on the VM vs 31.9 secs on the laptop. The biggest speed improvement I experienced was with the Align Photos part of the workflow, 17 secs vs 40 secs.
I have only tried this with a small set of 28 photos, so maybe bigger savings come with more photos, but I am curious to know other people's experience, especially with the Nvidia M60.
Thanks!