Photogrammetry Testing 11: VisualSFM & OpenMVS (and COLMAP+openMVS)


Here’s the original post, with links to all posts

This post was updated 27/5/17 – see end of post; turns out using COLMAP for sparse reconstruction let openMVS create an awesome mesh.

Normally I see OpenMVS mentioned in the same breath as OpenMVG, but I’m still having significant troubles with OpenMVG behaving with this dataset, so I’ll run the matching with VisualSFM.

Download VisualSFM here:

And Download OpenMVS binaries here: (or build it yourself from source.)

It’s a while since I’ve used VisualSFM, preferring COLMAP recently.  My word it is fast!

95 seconds to a full reconstruction of all 53 images:


Then SfM->Save nvmatch.

I’ve saved it in a folder called “work”, and named the file “Styrac.nvm”.  My openMVS binaries are in a folder at the same level as work, called openMVS_binaries.

I’m running the following commands through powershell in windows.

Convert Scene:

..\openMVS_binaries\InterfaceVisualSFM.exe -i .\styrac.nvm

This was super quick.

Densify point cloud:

..\openMVS_binaries\DensifyPointCloud.exe -I styrac.mvs


Here’s what the dense cloud looks like:


It’s… well, it’s pretty noisy.

Time taken: 17 minutes 6 seconds.

Mesh reconstruction

We can run this on either the sparse or the dense point cloud.

..\openMVS_binaries\ReconstructMesh.exe styrac_dense.mvs

I also ran this on the sparse cloud just for interest sake, and because the dense cloud was so noisy:


Mesh Refinement

After reconstructing the mesh, we refine it.  Again, I’ve done this on both the sparse and dense cloud:

..\openMVS_binaries\RefineMesh.exe styrac_dense_mesh.mvs –resolution-level 2

It’s worth noting that on my 16Gb ram machine, I ran out of memory while refining the mesh for the sparse cloud so needed to add the “resolution-level 2″ to reduce memory usage.


The sparse cloud has actually resulted in a mesh not too dissimilar to the dense cloud.


Use the command:

..\openMVS_binaries\TextureMesh.exe styrac_dense_mesh_refine.mvs


Concluding remarks:

Well, sadly I didn’t manage to get amazing results.  It looks like things fell apart on the Densify Point Cloud stage, and perhaps with a bit more digging I’ll be able to improve the results.

What is noteworthy is that meshing the sparse cloud actually resulted in a reasonably nice model, especially when textured, and could perhaps be useful for “quick’n’dirty” models.

As always, if time permits I’ll play a little more and see if I can improve these results.  For now though, ColMap remains my Goto.

UPDATE 27/5/17

Turns out what you use for the sparse reconstruction matters.  Even though VisualSFM looked good at the camera matching stage, it was the reason the final mesh from openMVS wasn’t great. The thread with the author of openMVS, at the sketchfab model above inspired me to try a different matching software.  So I ran COLMAP and exported the cameras + sparse cloud as a *.nvm file, then did exactly the same with openMVS as described above.  This was the result:

I’m sure you’ll agree that this is probably about the best model produced yet in this series!


5 thoughts on “Photogrammetry Testing 11: VisualSFM & OpenMVS (and COLMAP+openMVS)

  1. Great work comparing and documenting all these photogrammetry solutions! Regarding this post in particular, I can complement it with some minor comments:
    – indeed, OpenMVS is usually mentioned together with OpenMVG, thought the two projects have nothing in common, except that the creators of the two projects are friends
    – OpenMVS is responsible only for the dense reconstruction part (dense point-cloud, mesh, texture) of the pipeline, and the quality of these outputs depend a lot on the accuracy of the camera calibration supplied as input; in this case, the problem is the VisualSFM input as discovered and discussed in more detail here:
    – OpenMVS project was designed and developed to deliver high quality textured meshes as the end result (for many reasons, not here the place to discuss); the dense point-cloud is only an intermediate step towards that goal and it was never meant to be used alone; do not be fulled by it’s noisiness, it is know to be like that and that does not affect the end result

    1. Yeah, the dense cloud only as an intermediary caught me out, having been used to pipelines where the dense point cloud is generally clean and an end in and of itself.

      Thank you!

    1. Thanks. Is that just camera matching/sparse reconstruction? I’m pretty burned out from all this for now (and I need to spend some time actually processing real data, instead of just testing), but perhaps in the future I’ll take a look.

      1. sparse + depth map reconstruction. Yeah, it seems like you found the perfect photogrammetry pipeline for now (Colmap + openMVS) … Thanks for the testing/overview btw.

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