You... you must have been quite some 5 year old.
Now, perhaps referring to differentiability isn't layperson-accessible, but this is HN after all. I found it to be the perfect degree of simplification personally.
How about this:
Take a lot of pictures of a scene from different angles, do some crazy math, and then you can later pretend to zoom and pan the camera around however you want
- point cloud - fuzzy ellipsoid - view-dependent color - spherical harmonics - low order - differentiable renderer (what makes it differentiable? A renderer creates images, right?) - subtract the resulting image from the ground truth (good to know this means your original photos, but how do you subtract images from images?) - millions of ellipsoid parameters (the explanation previously mentioned 4 parameters by name. Where are the millions coming from?) - gradient descent (I've heard of this in AI, but usually ignore it because I haven't gotten deep enough into it to need to understand what it means) - 3D point cloud's positions (are all point clouds 3d? The point cloud mentioned earlier wasn't. Or was it? Is this the same point cloud?)
In other words, you've explained this at far too high a level for me. Given that the request was for ELI5, I expected an explanation that I could actually follow, without knowing any specific terminology. Do disregard specifics and call it math. Don't just call it math and skip past it entirely: call it math and explain what you're actually doing with the math, rather than trying to explain the math you're doing; same for all the other words. If a technical term is only needed once in a conversation, then don't use it.
Given that I actually do know what photogrammetry is at a basic level, I can make a best-effort translation here, but it's purely from 100% guessing rather than actually understanding:
1. Create a 3d scan of a real-life scene or object. It uses radar (intentionally incorrect term, more familiar) or multiple photographs at different angles to see the 3 dimensional shape.
2. For some reason, break up the stapes into smaller shapes.
This is where my understanding goes to nearly 0:
3-5: somehow, looking at the difference between a rendering of your 3d scene and a picture of the actual scene allows you to correct the errors in the 3d scene to make it more realistic. Using complex math works better and having the computer do it is less effort than manually correcting the models in your 3d scene.