Month: June 2019

A useful tool was recommended to me by a former coworker who works at this new startup called Bellus3d. They use the depth sensor in the iPhone (with face ID logins) along with the camera and generate this model. Their app looks good. Easy to use.

They had a web UI to view the model in 3D. You can move it around.

I could import this to Autodesk Meshmixer and 3D print it.

There are 46,000 vertices and 90,000 triangles. My 3d printer isn’t all that accurate, I get an effective resolution of 1.0mm to 1.5mm, so I am going to reduce complexity. It also helps reduce filament threads. See https://re3d.org/reduce-stl-triangles-using-autodesk-meshmixer/

Edit -> Make Solid. Collapse triangle mesh. I use cell size of 1.8mm and mesh density 1.4mm.

Came across some photogrammetry software lately. I had an idea to try them out by taking a bunch of photos and generate a 3d model.

But it’s difficult. Tools I tried were COLMAP and visualSFM. Seems to be bad results. I think I had insufficient number of photos, and there weren’t enough correlated features.

I thought “well what if I used a video”, so I took a video of my mouse, and dump them into images.

But a lot of them were blurry, like this.

So I thought there should be a way to filter out the blurry images. Such as using

• Fast Fourier Transform
• Laplace (or LoG) filter

There were some suggestions on StackOverflow that hint OpenCV is a good tool. I found some blurry detector on github https://github.com/indyka/blur-detection but there was some issues. For one, the threshold computed varied too greatly and so I had to manually figure out what were the 80th percentile and better (sharper, higher score) images. But some images contained no content, such as a blank table. It would be classified as sharp… so if going down this method, you might want to remove those blank images.