A year ago, teenagers at @hackclub set out to build the greatest math game ever. Today, SineRider enters public beta. Experience a mesmerizing world of puzzles infused with love and inspired by the TI-84 🔎. Hello, world 💖 this is sinerider.com

I do a lot of work in this area. To start off, both methods are used for 3d reconstruction, or the ability to fully reconstruct a scene from any perspective given a few images of the scene. For people with no graphics background: nerfs predict the color at each individual pixel (point on the screen), whereas splats “splat” a bunch of colored blobs together until the picture is made. For people with some tech/gaming background: nerfs use ray tracing to create a scene, whereas gaussian splats use rasterization create a scene. they both use machine learning to “remember” the colors in the scene to rasterize or ray trace. For people with a tech background: nerfs are neural networks that predict each pixel’s color given a ray direction while splats create and modify millions of colored/transparent blobs (3d gaussians) until they form a scene using gradient-based optimization. both are trained from a limited number of viewpoints of a scene (the training data), and allows “novel view synthesis” (the ability to view the scene from new viewpoints not included in the training data). Still oversimplifying but if there’s interest I can expand on this in a blog post.