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. The conceptual simplicity of our approach enables a
number of benefits, such as support for time-varying scalar fields, optimizing
to preserve spatial gradients, and random-access field evaluation. We study the
impact of network design choices on compression performance, highlighting how
simple network architectures are effective for a broad range of volumes.

本文提出了一种使用隐式神经表示法来压缩三维标量场的方法，通过将标量场表示为学习到的函数，通过设置神经网络的权重数小于输入大小，压缩表示方法成为函数逼近，结合网络权重的精细量化，实现了高度紧凑表示，胜过了现有的体积压缩方法，并对网络设计选择对压缩性能的影响进行了研究。