Compressive Neural Representations of Volumetric Scalar Fields

Understand

We present an approach for compressing volumetric scalar fields using implicit neural representations.

• Our approach represents a scalar field as a learned function, wherein a neural network maps a point in the domain to an output scalar value.
• By setting the number of weights of the neural network to be smaller than the input size, we achieve compressed representations of scalar fields, thus framing compression as a type of function approximation.
• Combined with carefully quantizing network weights, we show that this approach yields highly compact representations that outperform state-of-the-art volume compression approaches.