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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HiLSVA introduces a plan-first multi-agent LLM system for scientific visualization that incorporates explicit human oversight, stepwise provenance, and learn-at-test-time adaptation, evaluated via case studies and a 12-participant user study.
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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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HiLSVA: Design and Evaluation of a Human-in-the-Loop Agentic System for Scientific Visualization
HiLSVA introduces a plan-first multi-agent LLM system for scientific visualization that incorporates explicit human oversight, stepwise provenance, and learn-at-test-time adaptation, evaluated via case studies and a 12-participant user study.