Neural Field Training

Train implicit neural representations (NeRF, Instant NGP, tri-plane) that encode 3D scenes as continuous functions mapping spatial coordinates to density and color, optimized from multi-view images. Neural fields represent a 3D scene as a neural network that maps a 3D coordinate (and optionally viewing direction) to density and color. Training uses differentiable volume rendering: for each pixel, points along the camera ray are sampled, their density and color are evaluated by the network, and alpha-composited to produce a pixel color. The loss between rendered and ground truth pixel colors drives the optimization. Modern variants use efficient grid-based representations (Instant NGP), tri-plane hybrid representations (EG3D), or hash encoding for fast training.