Most VLMs treat images as a flat grid of 2D pixels that get mapped into the same token space as text. That’s great at semantic pattern matching and identifying objects, but they lack a good way to represent depth, volume, and geometry. Recognizing what an object looks like in 2D is very different from understanding how it occupies 3D space.