Efficient Neural Representation of Volumetric Data using
  Coordinate-Based Networks
Sudarshan Devkota Sumanta Pattanaik
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.