Efficient Neural Representation of Volumetric Data using Coordinate-Based Networks.

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dc.contributor.authorDevkota, Sudarshanen_US
dc.contributor.authorPattanaik, Sumanten_US
dc.contributor.editorChaine, Raphaëlleen_US
dc.contributor.editorDeng, Zhigangen_US
dc.contributor.editorKim, Min H.en_US
dc.date.accessioned2023-10-09T07:34:56Z
dc.date.available2023-10-09T07:34:56Z
dc.date.issued2023
dc.description.abstractIn this paper, we propose an efficient approach for the compression and representation of volumetric data utilizing coordinatebased 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 multiresolution 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.en_US
dc.description.number7
dc.description.sectionheadersVolumetric Reconstruction
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume42
dc.identifier.doi10.1111/cgf.14955
dc.identifier.issn1467-8659
dc.identifier.pages14 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.14955
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14955
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Human-centered computing -> Visualization; Computing methodologies -> Image compression
dc.subjectHuman centered computing
dc.subjectVisualization
dc.subjectComputing methodologies
dc.subjectImage compression
dc.titleEfficient Neural Representation of Volumetric Data using Coordinate-Based Networks.en_US
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