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| 3D Scanning |
3D scanning is a very wide field, including many, many different use-cases. I personally like to look at the ideas around using 3D scanning as another modal for allowing people to interact with the world around them, I'm talking about museums 3D scanning all of the works they hold, or capturing high quality 3D scans of monuments that are at risk of being lost forever, and I think education in general can be greatly aided by introducing more active methods of learning based on working in 3D.
In the past, it has been prohibitively expensive and has had many drawbacks. It used to be that capturing a 3D model meant expensive equipment and prep time for your model, every surface to be captured needed to be perfectly matte or even had to be covered in a partical pattern. Photogrammetry and the wider concept of neural radiance fields have introduced a more software-defined approach to 3D scanning. NeRF, MIP-NeRF, 3D Gaussians, and more techniques show off the ability to use neural networks to define a complete 3D model from a 2D reference point.
I should say that none of these techniques are anywhere near a "production ready" stage, you still cannot derive an accurate 3D mesh from these techniques, but they have brought a lot of interesting concepts forward. For one, being able to share a color-accurate model has been the focus of my research recently, using what are called 3D guassians you can use an entirely software-defined approach to create very light (the below model is 6MB) and high quality 3D models.
Below you can see an example of a 3D guassian using the method "splatfacto", created by the engineers working on the nerfstudio project inspired by the SIGGRAPH paper "3D Gaussian Splatting for Real-Time Rendering of Radiance Fields".
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Below are some pre-rendered scenes based on the nerfacto method also developed by nerfstudio. Though I have found that in some context the "splatting" method produces a smaller file and is more efficient to run, NeRF still provides pretty excellent quality.
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