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

翻译：对大规模三维科学数据集进行可视化需要在性能与保真度之间取得平衡，但传统工具往往对专业技术要求过高。我们提出UnrealVis，一个用于在交互式探索过程中配置与评估渲染技术的虚幻引擎优化实验室。在综述55篇论文后，我们建立了涵盖6大系列22种优化技术的分类体系，并通过引擎子系统（如Nanite、细节层次（LOD）方案与剔除技术）实现这些技术。该系统提供包含实时遥测和A/B对比的直观工作流，支持局部与全局性能分析。通过核糖体结构及体积流场的案例验证，结合专家评估，UnrealVis有助于选择在满足性能目标的同时保持结构保真度的优化组合。UnrealVis的开源代码可在https://github.com/XAIber-lab/UnrealVis 获取。