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.

翻译：本文提出了一种利用坐标网络与多分辨率哈希编码实现体数据压缩与表示的高效方法。体数据的高效压缩对于医学成像和科学模拟等众多应用至关重要。本方法通过学习空间坐标与强度值之间的映射关系，实现了有效压缩。我们比较了不同编码方案，证明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。此外，我们采用基于优化的元学习（具体使用Reptile算法）来学习针对体数据量身定制的神经表示权重初始化，从而在优化过程中实现更快的收敛。我们还与当前最先进的方法进行了对比，展示出改进的图像质量和压缩比。这些发现凸显了坐标网络与多分辨率哈希编码在高效精确表示体数据方面的潜力，为大规模数据可视化及其他应用的进步铺平了道路。