In this paper, we propose an efficient approach for the compression and
representation of volumetric data utilizing coordinate-based networks and
multi-resolution hash encoding. Efficient compression of volumetric data is
crucial for various applications, such as medical imaging and scientific
simulations. Our approach enables effective compression by learning a mapping
between spatial coordinates and intensity values. We compare different encoding
schemes and demonstrate the superiority of multi-resolution hash encoding in
terms of compression quality and training efficiency. Furthermore, we leverage
optimization-based meta-learning, specifically using the Reptile algorithm, to
learn weight initialization for neural representations tailored to volumetric
data, enabling faster convergence during optimization. Additionally, we compare
our approach with state-of-the-art methods to showcase improved image quality
and compression ratios. These findings highlight the potential of
coordinate-based networks and multi-resolution hash encoding for an efficient
and accurate representation of volumetric data, paving the way for advancements
in large-scale data visualization and other applications.

我们提出了一种利用坐标网络和多分辨率哈希编码对体积数据进行压缩和表示的高效方法，通过学习空间坐标和强度值之间的映射，实现有效的压缩，比较了不同编码方案，并展示了多分辨率哈希编码在压缩质量和训练效率方面的优越性，同时借助优化型元学习，特别是使用 Reptile 算法，学习适用于体积数据的神经表示的权重初始化，从而在优化过程中实现更快的收敛，此外，我们与最先进的方法进行比较，展示了改进的图像质量和压缩比，这些发现突显了利用基于坐标的网络和多分辨率哈希编码进行高效准确的体积数据表示的潜力，为大规模数据可视化和其他应用的进展铺平了道路。

基于坐标的网络在体积数据的高效神经表示

Efficient Neural Representation of Volumetric Data using  Coordinate-Based Networks

Coordinate-based networks have emerged as a powerful tool for 3D
representation and scene reconstruction. These networks are trained to map
continuous input coordinates to the value of a signal at each point. Still,
current architectures are black boxes: their spectral characteristics cannot be
easily analyzed, and their behavior at unsupervised points is difficult to
predict. Moreover, these networks are typically trained to represent a signal
at a single scale, so naive downsampling or upsampling results in artifacts. We
introduce band-limited coordinate networks (BACON), a network architecture with
an analytical Fourier spectrum. BACON has constrained behavior at unsupervised
points, can be designed based on the spectral characteristics of the
represented signal, and can represent signals at multiple scales without
per-scale supervision. We demonstrate BACON for multiscale neural
representation of images, radiance fields, and 3D scenes using signed distance
functions and show that it outperforms conventional single-scale coordinate
networks in terms of interpretability and quality.

通过引入具有解析傅里叶光谱的频限坐标网络（BACON）构架，该研究论文成功演示了用于多尺度神经表示图像、辐射场和三维场景的 BACON 网络在可解释性和质量方面优于传统的单尺度坐标网络。