UnrealVis: A Testing Laboratory of Optimization Techniques in Unreal Engine for Scientific Visualization

TL;DR

UnrealVis is an Unreal Engine-based testing environment that helps scientists optimize rendering techniques for large 3D datasets, balancing performance and fidelity with an intuitive workflow.

Contribution

It introduces a comprehensive taxonomy of 22 optimization techniques, an interactive workflow, and validation through case studies, advancing scientific visualization tools.

Findings

Validated with ribosomal and flow field datasets

Enables selection of optimization techniques balancing performance and fidelity

Provides an accessible platform for performance analysis in scientific visualization

Abstract

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