- #Meshlab generate mesh from point cloud update#
- #Meshlab generate mesh from point cloud software#
- #Meshlab generate mesh from point cloud code#
#Meshlab generate mesh from point cloud code#
I refactored a bit of code from StackOverflow (as one does) with a slightly different set of gradients. Picking and combining explanations from the posts by Adrian Biagioli and Raouf did work out somehow. The hard part is understanding the algorithm since there are a lot of explanations of varying quality on differnet algorithms (new and classic). That’s one of the many usecases of Perlin noise: generate landscapes in games.Īlternatives to classic or improved Perlin noise are apparently Value noise and Simplex noise, but I just went with the classic flavour. In the two-dimensional case you get a pretty nice landscape-like output with hills and valleys (but no caves, no overhangs). That’s a pretty simple way of generating continuous noise patterns on a plane, in a 3d space or any other dimension. Some generated pattern which could be imprinted on some parts of the object while not creating any issues with geometries that are required for functionality and still being (somewhat) printable.ĭisclaimer: I started this inquiry with very little knowledge about 3d stuff (point clouds, meshes and surface reconstruction algorithms) and there may be way better solutions if you’ve got a basic understanding of these topics. Here's how well that went: VSFM application failed to build, halting.Recently I spent a bit of time thinking about visually improving non-functional areas of a 3d-printed part.
#Meshlab generate mesh from point cloud update#
Update #2: Tried installing VSFM for the dense stereo reconstruction, but no installers are provided so I have to build it.
#Meshlab generate mesh from point cloud software#
So now I am researching whether there is other software that can fill this gap, or a way to hack COLMAP to use the CPU. I guess they're telling me to go buy a new computer. They offer the poor excuse that doing it on a CPU would be slow. Unfortunately, the developers of COLMAP did something not nice by providing no way to perform dense reconstruction without CUDA. It explains in detail how sparse reconstruction is the first part of the process, and dense reconstruction is the second part. Update: I've found a much better tutorial than the others I had been following. ply file exported from COLMAP into MeshLab I can distinguish the colors in the vertices when I crank up the Point Size slider.īut it's only showing vertex colors and since there are no faces on my geometry I'm stuck, not knowing whether it's possible to generate faces or use this 3D data at all. I expected results more like this (taken from a video tutorial):Ĭlearly I'm doing something very wrong, because COLMAP doesn't produce anything close to this for me.
Can you see that yellow thing sticking up in the point cloud? That's the object I attempted to capture. There must be something else causing my captures to be poor.Ĭamera positions are represented in red, and since I used so many frames of data (nearly 500) in hopes of getting usable results, it shows as one big red solid line. So I am now ruling out a problem of not enough frame data. After not getting good results with that I tried increasing the FPS in various increments, even up to 30fps (which is what I shot at, and I think is overkill). Everything seems to be high enough quality as far as the source images are concerned.Īt first captured 1 PNG per second of footage. Next I saved still PNG images from the resulting 4K video. (iPhone's built-in AE/AF Lock feature was leaving many shots out of focus, but the app solved that.) In order to get consistent exposure I bought an iPhone app called ProMovie+ that can shoot in 4K with the exposure locked but will focus on the subject as needed. I've studied various YouTube tutorials, but the results I get differ from what I am seeing, mainly in that I can never get a solid 3D mesh. I've been trying to use COLMAP to produce 3D meshes from photographs, but all I can get is useless point clouds.
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