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