An explicit model using learned 3D Gaussians for volume compression encodes geometry explicitly and outperforms implicit neural representations on unstructured volumes with faster training.
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2026 2verdicts
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RaFI is a new framework providing a simple interface for forwarding work items between GPUs in multi-node multi-GPU data-parallel computing using CUDA and MPI.
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Efficient Compression of Structured and Unstructured Volumes via Learned 3D Gaussian Representation
An explicit model using learned 3D Gaussians for volume compression encodes geometry explicitly and outperforms implicit neural representations on unstructured volumes with faster training.
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RAFI -- A Ray/Work Forwarding Infrastructure for Data Parallel Multi-Node/Multi-GPU Computing
RaFI is a new framework providing a simple interface for forwarding work items between GPUs in multi-node multi-GPU data-parallel computing using CUDA and MPI.