Interactive Volume Visualization via Multi-Resolution Hash Encoding based Neural Representation

• URL: http://arxiv.org/abs/2207.11620v3
• Date: Thu, 29 Jun 2023 20:35:50 GMT
• Title: Interactive Volume Visualization via Multi-Resolution Hash Encoding based Neural Representation
• Authors: Qi Wu, David Bauer, Michael J. Doyle, Kwan-Liu Ma
• Abstract summary: We show that we can interactively ray trace volumetric neural representations (10-60fps) using modern GPU cores and a well-designed rendering algorithm. Our neural representations are also high-fidelity teracell (PSNR > 30dB) and compact (10-1000x smaller) To support extreme-scale volume data, we also develop an efficient out-of-core training strategy, which allows our neural representation training to potentially scale up to terascale.
• Score: 29.797933404619606
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural networks have shown great potential in compressing volume data for visualization. However, due to the high cost of training and inference, such volumetric neural representations have thus far only been applied to offline data processing and non-interactive rendering. In this paper, we demonstrate that by simultaneously leveraging modern GPU tensor cores, a native CUDA neural network framework, and a well-designed rendering algorithm with macro-cell acceleration, we can interactively ray trace volumetric neural representations (10-60fps). Our neural representations are also high-fidelity (PSNR > 30dB) and compact (10-1000x smaller). Additionally, we show that it is possible to fit the entire training step inside a rendering loop and skip the pre-training process completely. To support extreme-scale volume data, we also develop an efficient out-of-core training strategy, which allows our volumetric neural representation training to potentially scale up to terascale using only an NVIDIA RTX 3090 workstation.

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