Neural implicit fields (or Implicit Neural Representations - INRs) are a machine learning paradigm that represents data—such as 3D shapes, images, or scenes—as a continuous function parameterized by a neural network rather than discrete grids or pixels. They map coordinates (e.g. (𝑥,𝑦,𝑧) ) to signal values (e.g., color, density, or distance), enabling high-fidelity, compact storage, and resolution-independent rendering of complex data.