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Hi tweezlednutball,
Indeed, since a while, the dense cloud generation (2d step of dense cloud stage) has changed a bit. The limit of simultaneous pictures taken into account for it (called depth filtering limit), which was 50 before, as been removed.
So if you handle modeling from a set of pictures with a high level of redundancy (number of pictures that look toward the same place), it increased a lot the calculation time because PhotoScan try to filter with a super high amount of data.
With the following piece of code (given by Alexey in another topic), you will be able to define your own limit (N) before starting the dense cloud processing. Be careful not to use a value lower than 50-60 to keep a correct filtering.Code: [Select]PhotoScan.app.settings.setValue('main/depth_filtering_limit', N)
Regards
Hello tweezlednutball,
Thanks for reporting.
It will be fixed in the next 1.4 update.
Currently GPU can be utilized for image matching (first step of Align Photos stage - includes feature points detection, pre-selection and image matching itself) and depth maps reconstruction (first step of Dense Cloud Stage).
Version 1.4 is using GPU for Mesh Refinement operation and during the new Build Mesh method (also for depth maps reconstruction step).
Other processing steps are performed on CPU only.
ive scanned most of the major cities in the world using photoscan
"Scan" implies an active active sensing method, which is not an accurate term for photography. You computed a statistics based pixel matching model for those cities, you did not scan them.
