UnrealVis: A Testing Laboratory of Optimization Techniques in Unreal Engine for Scientific Visualization
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914442968563712 |
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| author | Filosa, Matteo Nardocci, Andrea Catarci, Tiziana Angelini, Marco |
| author_facet | Filosa, Matteo Nardocci, Andrea Catarci, Tiziana Angelini, Marco |
| contents | Visualizing large 3D scientific datasets requires balancing performance and fidelity, but traditional tools often demand excessive technical expertise. We introduce UnrealVis, an Unreal Engine optimization laboratory for configuring and evaluating rendering techniques during interactive exploration. Following a review of 55 papers, we established a taxonomy of 22 optimization techniques across six families, implementing them through engine subsystems such as Nanite, Level of Detail(LOD) schemes, and culling. The system features an intuitive workflow with live telemetry and A/B comparisons for local and global performance analysis. Validated through case studies of ribosomal structures and volumetric flow fields, along with an expert evaluation, UnrealVis facilitates the selection of optimization combinations that meet performance goals while preserving structural fidelity. UnrealVis is available at https://github.com/XAIber-lab/UnrealVis |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_02980 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | UnrealVis: A Testing Laboratory of Optimization Techniques in Unreal Engine for Scientific Visualization Filosa, Matteo Nardocci, Andrea Catarci, Tiziana Angelini, Marco Human-Computer Interaction Visualizing large 3D scientific datasets requires balancing performance and fidelity, but traditional tools often demand excessive technical expertise. We introduce UnrealVis, an Unreal Engine optimization laboratory for configuring and evaluating rendering techniques during interactive exploration. Following a review of 55 papers, we established a taxonomy of 22 optimization techniques across six families, implementing them through engine subsystems such as Nanite, Level of Detail(LOD) schemes, and culling. The system features an intuitive workflow with live telemetry and A/B comparisons for local and global performance analysis. Validated through case studies of ribosomal structures and volumetric flow fields, along with an expert evaluation, UnrealVis facilitates the selection of optimization combinations that meet performance goals while preserving structural fidelity. UnrealVis is available at https://github.com/XAIber-lab/UnrealVis |
| title | UnrealVis: A Testing Laboratory of Optimization Techniques in Unreal Engine for Scientific Visualization |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2604.02980 |