sarlinpe
commented
2 years ago Hi @hturki, Nice to see that you're giving it a try on larger datasets :)
First, about the data: it seems that the connectivity of your aerial images is much higher than we usually have in street-level imagery. You can see that 10%-20% of the tracks have more than 20 keypoints. This is definitely more challenging for the keypoint adjustment. To speed this up, you can switch to the topological_reference strategy:
sfm = PixSfM(conf={"dense_features": {"use_cache": True}, "KA": {"strategy": "topological_reference"}})This reduces the number of optimization constraints and is thus much faster (linear vs quadratic) but less accurate. It is described in Appendix B3 of the paper.
Even with >100GB of RAM, you might later run into trouble with the bundle adjustment. If this happens, I suggest to switch to the costmaps strategy (described in Appendix C):
sfm = PixSfM(conf={
"dense_features": {"use_cache": True},
"KA": {"strategy": "topological_reference"},
"BA": {"strategy": "costmaps"},
})These tweaks are also described in the Large-scale refinement section of the README.
Let me know how this helps, I am also curious to see how far we can push this.
hturki
Phil26AT
First of all, thanks for the great work. In the paper and project page the authors mention scaling PixSfM to thousands of images. I'm however running into issues doing the same with datasets ranging in the 1600-3000 image range. Two of the datasets in question can be found at: https://meganerf.cmusatyalab.org/#data
My code looks roughly like:
And in particular the keypoint adjustment phase seems to be taking a very long time. Some examples so far:
Another example seems to be getting slower over time:
And same for a third example
The specs for the machines across each of these runs varies a bit, but they're pretty powerful machines (>100GB of ram, dozens of cores, SSD mounts)
I've also tried running PixSfM on an even larger example (matching only against the 20 closest neighbors instead of 50), where keypoint adjustment throws an exception. I've trying reducing the output dim and patch size to no avail:
Any thoughts on what I might be doing wrong, or is a week+ runtime to be expected no matter what? I've gotten suboptimal results on these (drone imagery-related) datasets using even commercial photogrammetry software, so excited to see if I can get better results with your featuremetric approach!