What is implicit neural representation (INR) in computer vision?

INRs represent images or 3D scenes as continuous functions learned by neural networks.

Implicit Neural Representations encode signals (images, shapes, or scenes) as continuous functions mapping coordinates to values (color, density, etc.). Instead of storing discrete grids, INRs learn smooth continuous mappings using MLPs. This allows infinite resolution rendering and compact representation of complex geometry.