3 ms·
I found it neat that it really can make use of all the cpu and memory you can throw at it. I used it to stich around 1000 photos, and watched as it maxed my 32
by dsmmcken 4y ago
I found it neat that it really can make use of all the cpu and memory you can throw at it. I used it to stich around 1000 photos, and watched as it maxed my 32 cores and 64gb ram in my workstation for hours.
I wanted to annotate a map of my property to keep track of the fruit trees I am planting. Google maps resolution for the area is extremely low, but a basic DJI drone, pix4d capture (free for capture) and ODM can give you a nice detailed imagery with very little work. The difference was being able to see a stand of trees vs individuals.
- carbadtraingood 4y agoIt's great, but several times it does fall back to single threaded performance. I regularly use it to generate photos of hundreds of acres, and I end up needing a machine with more RAM and less CPU.
- pierotofy 4y agoYep, RAM is usually the bottleneck for processing larger datasets. We do have a divide-and-conquer approach to push the limits of your hardware though: https://docs.opendronemap.org/large/ https://docs.opendronemap.org/large/
- carbadtraingood 4y agoI found, in practice, that the divide and conquer approach led to weird altitude artifacts, where different divisions would result in different heights, so the point clouds didn't line up. Could have been a one off bug though.
- pierotofy 4y agoIt can happen; point cloud alignment between submodels can be tricky. Sometimes increasing the amount of split overlap can help, but not always.
- davijo 4y agoAmazing piece of software! I've a question. It's a bit unclear to me, is there a global adjustment applied to the sub-blocks that would help with the overlap issues?
- pierotofy 4y agoThere is; the alignment uses affine transformations, but it cannot account for certain cases (e.g. severe bowling effects). It's an interesting area of research.