Efficient Neural Representation of Volumetric Data using Coordinate-Based Networks
This work addresses the need for efficient compression in applications like medical imaging and scientific simulations, though it is incremental as it builds on existing coordinate-based network and encoding techniques.
The paper tackles the problem of compressing volumetric data efficiently by using coordinate-based networks with multi-resolution hash encoding and meta-learning for faster convergence, resulting in improved image quality and compression ratios compared to state-of-the-art methods.
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.