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.

中文翻译：

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体积标量场的压缩神经表示

我们提出了一种使用隐式神经表示压缩体积标量场的方法。我们的方法将标量场表示为学习函数，其中神经网络将域中的一个点映射到输出标量值。通过将神经网络的权重数设置为小于输入大小，我们实现了标量场的压缩表示，从而将压缩框架化为一种函数逼近。结合仔细量化网络权重，我们表明这种方法产生了优于最先进的体积压缩方法的高度紧凑的表示。我们的方法在概念上的简单性带来了许多好处，例如支持时变标量场、优化以保留空间梯度和随机访问场评估。