After some test of ORB, Sift and Hahog I wonder why sift is still the default.
As far as I’ve seen now Hahog with flann is way faster than Sift and the resulting pointcloud denser than with Orb. According to Sajin Hahog also is about as good as Sift when it comes to getting a correct camera calibration.
I’m running more test though but it seems Hahog is the best of both worlds.
Did you checkout the correct branch?
git clone https://github.com/pierotofy/ODM
cd ODM
git checkout 300
Then build.
Thats a separate conversation and not a breaking change to the API, though could be compelling. Maybe start a thread to discuss or open an issue to discuss on the ODM repo.
Hi Adam’s face, meet Adam’s palm! Built, test results on the way - using a skinny tower dataset from a couple of years ago…
Oooh, good test case.
Initial results - I see a little more definition on the tower than before ( Modelling skinny tall things - an observation - #14 by adamsteer )
Now setting some noise filters and running again.
I did notice that in CloudCompare, mesh colors are rendered ‘under’ the model. May be a function of my ‘built on this machine’ cloudcompare, or this particular model?
Best effort for now - adding filters, cleaning up a bit. What I really need to do is go back to this site and collect far more images. 140 isn’t enough.
I think the definition of small features on the tower and fences is improved quite a bit. Be great to see results from more thoughtfully-collected datasets. IIRC Sensefly had a public dataset from a cell tower, if I can find it, I can try it out… For now I have to go do my paid job 
Another tall thin structure if you are still looking for them to test, a 33kV power pole.
No lattice work though, the most difficult to work with! but there are thin wires that it would be nice to see, and another wire for the stay which is partially missing, just so people don’t think it is floating in mid air 
M2P images and geo.json file here-
I recently ran it again, better in some respects but worse in others, than previous attempts.
294 images 36:41:38
|Options:|auto-boundary: true, bg-removal: true, boundary: {“type”:“FeatureCollection”,“features”:[{“type”:“Feature”,“properties”:{},“geometry”:{“type”:“Polygon”,“coordinates”:[[[151,-31 etc]]], feature-quality: ultra, gps-accuracy: 6, mesh-octree-depth: 13, mesh-size: 600000, min-num-features: 20000, optimize-disk-space: true, orthophoto-resolution: 0.5, pc-geometric: true, pc-quality: ultra, resize-to: -1, skip-orthophoto: true, use-3dmesh: true, verbose: true|
|Average GSD:|0.22 cm|
|Area:|185.9 m²|
|Reconstructed Points:|173,198,614|
The PC is a bit more complete, but also missing the wires and top, and the cross bar is a bit patchy too.
Initial run - better definition on the arm holding wires, with a ‘ghost pole’ - some tuning will fix that
So far so good for ODM3 proposals…
Thanks for running it Adam, I haven’t had time What settings did you use?
I’ve been having some fun with ODM 3.0.0
3.0.0 (Courtesy of Stephen):
3.0.0:
3.0.0:
3.0.0:
3.0.0:
3.0.0:
3.0.0:
3.0.0:
3.0.0
These are so fun.
I finally got a chance to put one of my datasets through 3.0.0 and I’m pleased!
I can finally run ultra/ultra! It took 18 hours for 311 photos, but it ran fine on my M1 with 56G of RAM and 8 CPUs assigned to Docker. Also, the performance over water has improved – my pond surface is probably smooth enough for me to get volume estimates now.
There were fewer changes to CLI options than I had expected, which is pretty nice. I will look at the GUI later!
Those red fence panels, leaning against the building, have lots better detail don’t they
Does the new engine warrant an update to the tutorial section for High Quality Orthophotos? Considering some of the older arguments have been removed, is there a new list of ortho-specific options/flags I should use if I want to create the highest quality orthomosaic? I haven’t tested anything yet, but I would consider:
–orthophoto-resolution
–skip-3dmodel
–mesh-size
–pc-quality
–fast-orthophoto
–auto-boundary
–feature-quality (maybe?)
–sfm-algorithm (triangulation, maybe?)
Welcome!
Yes, likely a refresh is warranted.
I’d focus most on the point cloud, so pc-quality, feature-quality, pc-filter, and finally orthophoto-resolution.
Sometimes using the 3d model improved my orthos.
The sfm algorithm isn’t so much about quality as it is about increasing speed, and can cause quality issues of the data are not appropriate.
Would be fun with a new book!
It’s lovely to have but outdated.
Hmmm… That looks familiar… 
