[KaiserPro]

One of the key issues of "machine perception" is the inability of machines using standard image sensors to re-create the world accurately.

Lidars are great, and getting smaller, but they still eat a lot of power. (The quest 3 had a lidar on the front[well structured light] and it was mostly not used)

For machines to understand the 3d world, first they need to extract geometry, then isolate those geometries into objects. This method is _a_ way to do that, the first step, extracting 3d points.

The problem with this model is that the points are not actually that well aligned frame to frame. This is why it looks a bit blurry. I assume this is to avoid running out of memory, as you're not quite sure about which points are relevant and need to be kept in memory.

Once you have those points, you need to replace them with simplfied geometry, so that you can workout intersections and junk.