MLP-based Neural Fields and Rendering

Early neural fields typically adopt a multi-layer perceptron (MLP) as the global approximator of 3D scene geometry and appearance. They directly use spatial coordinates and viewing direction as input to the MLP and predict point-wise attribute, e.g. signed distance to scene surface (SDF), or density and color of that point.

Because of its volumetric nature and inductive bias of MLPs, this stream of methods achieves the SOTA performance in novel view synthesis.

The major challenge of this scene representation is that the MLP need to be evaluated on a large number of sampled points along each camera ray. Consequently, rendering becomes extremely slow, with limited scalability towards complex and large-scale scenes.

De