UnrealVis: A Testing Laboratory of Optimization Techniques in Unreal Engine for Scientific Visualization
This work addresses the problem of requiring excessive technical expertise for scientific visualization, offering a tool to optimize rendering for researchers and practitioners, though it is incremental as it builds on existing Unreal Engine capabilities.
The paper tackles the challenge of balancing performance and fidelity in visualizing large 3D scientific datasets by introducing UnrealVis, an Unreal Engine optimization laboratory that enables interactive exploration and evaluation of rendering techniques, validated through case studies like ribosomal structures and volumetric flow fields.
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