Not only is this a tool to create time machines to understand the history of our cities but to understand its economics, technicalities and social relationships, for a fairer society based on transparency. Our team believes we can use the buildings and rooms where we spend most of our lives, as sources of open source information to edit and share among citizens for community well-being. This workshop is part of the international research on “Architectural Democracy” based at Aalto University.
#Visualsfm meshlab how to
Participants should know how to use a computer and take photos!
Requirements are the participation fee (75 € for regular inscription and 50 € for students and ICOMOS members), your own laptop with wi-fi connection and a photo camera. We invite everyone to learn how to take photos and turn them into an interactive 3D model. A hands-on workshop, with experts from Portugal and Finland. So, in summary, a bit slower and a bit lower quality (and a bit more prone to user error) than MeshRecon, and certainly a lot more fiddly to use than Photoscan.Learn how to make an interactive 3D model of a compartment, building or urban scene with your own photo camera. On the other hand, needing to move between visualSFM and meshlab is a bit of a pain.
Remember though, that PMVS/CMVS hasn’t really been altered since I used in my 2012 (first submitted 2010) photogrammetry paper. ini file for VisualSFM includes options for PMVS, but I left them all at default), and altering the parameters for the Poisson surface reconstruction in meshlab might produce a marginally better model (though ultimately I think that’s the weakest part of this chain). : Compute Missing Pairwise Matching, finished Totally 0.000 seconds used. ini settings for PMVS may result in a better point cloud (The. On the one hand, I like the control playing with the. It also has some texturing issues on the right side of the model. Note that this was about the largest mesh in terms of number of faces. Note that this is not due to VisualSFM, but rather the dense reconstruction part PMVS/CMVS and meshing (meshlab), as can be seen from the quality of the MeshRecon mesh which used VisualSFM to match cameras.
#Visualsfm meshlab download
You can view and download the final model here:Ĭlearly this mesh does not compare well with either of the previous models. Mesh (Poisson Surface Reconstruction, Octree level 10): 95.18 seconds
Had I used a lower Octree depth, the model would have been smoother, but finer details would have been lost.ģD Camera Pose Reconstruction: 10 secondsĭense point cloud reconstruction: 703.2 seconds (2376394 faces) I generated a mesh using the Poisson Surface Reconstruction function, setting Octree depth to 10:īefore I could do that, I had to remove a few extraneous points that would really mess with the Poisson Surface Reconstruction, but this took only 203 seconds to select and delete the points:Īs you can see, the model has quite a bit of noise in the surface. The dense point cloud generated by CMVS/PMVS in VisualSFM The matching and reconstruction in VisualSFM This time, I’ve used VisualSFM again, followed by PMVS to generate the dense point cloud (a button in VisualSFM’s GUI allows this to happen nice and easily), and then a mesh was generated using the Poisson Surface Reconstruction in Meshlab. The model was situated on a glass desk, and there may be reflection issues from that. The model is about 12 cm in total length, has texture for the scales, and a reflective brass nameplate on the base. The dataset has 53 photos in total, and is available from this link. I’m using a dataset based on photographs of this Styracosaurus model (I’ve had it since I was quite young): I have previously outlined my goal of testing multiple photogrammetry solutions on a single dataset, and reporting times and results. Here’s the original post, and links to all posts
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