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

Multi-resolution hash encoding has recently been proposed to reduce the
computational cost of neural renderings, such as NeRF. This method requires
accurate camera poses for the neural renderings of given scenes. However,
contrary to previous methods jointly optimizing camera poses and 3D scenes, the
naive gradient-based camera pose refinement method using multi-resolution hash
encoding severely deteriorates performance. We propose a joint optimization
algorithm to calibrate the camera pose and learn a geometric representation
using efficient multi-resolution hash encoding. Showing that the oscillating
gradient flows of hash encoding interfere with the registration of camera
poses, our method addresses the issue by utilizing smooth interpolation
weighting to stabilize the gradient oscillation for the ray samplings across
hash grids. Moreover, the curriculum training procedure helps to learn the
level-wise hash encoding, further increasing the pose refinement. Experiments
on the novel-view synthesis datasets validate that our learning frameworks
achieve state-of-the-art performance and rapid convergence of neural rendering,
even when initial camera poses are unknown.

使用多分辨率哈希编码的联合优化算法可以同时校准相机位置和学习几何表示法，以提高神经渲染的性能和收敛速度。