Efficient Neural Representation of Volumetric Data using Coordinate-Based Networks

• URL: http://arxiv.org/abs/2401.08840v1
• Date: Tue, 16 Jan 2024 21:33:01 GMT
• Title: Efficient Neural Representation of Volumetric Data using Coordinate-Based Networks
• Authors: Sudarshan Devkota, Sumanta Pattanaik
• Abstract summary: We propose an efficient approach for the compression and representation of volumetric data using coordinate-based networks and hash encoding. Our approach enables effective compression by learning a mapping between spatial coordinates and intensity values.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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