Large industrial facilities such as particle accelerators and nuclear power plants are critical infrastructures for scientific research and industrial processes. These facilities are complex systems that not only require regular maintenance and upgrades but are often inaccessible to humans due to various safety hazards. Therefore, a virtual reality (VR) system that can quickly replicate real-world remote environments to provide users with a high level of spatial and situational awareness is crucial for facility maintenance planning. However, the exact 3D shapes of these facilities are often too complex to be accurately modeled with geometric primitives through the traditional rasterization pipeline. In this work, we develop Magic NeRF Lens, an interactive framework to support facility inspection in immersive VR using neural radiance fields (NeRF) and volumetric rendering. We introduce a novel data fusion approach that combines the complementary strengths of volumetric rendering and geometric rasterization, allowing a NeRF model to be merged with other conventional 3D data, such as a computer-aided design model. We develop two novel 3D magic lens effects to optimize NeRF rendering by exploiting the properties of human vision and context-aware visualization. We demonstrate the high usability of our framework and methods through a technical benchmark, a visual search user study, and expert reviews. In addition, the source code of our VR NeRF framework is made publicly available for future research and development.

翻译：大型工业设施（如粒子加速器和核电站）是科学研究与工业流程的关键基础设施。这些设施系统复杂，不仅需要定期维护与升级，且常因各种安全隐患而禁止人员进入。因此，能够快速复现真实远程环境、为用户提供高空间与场景感知能力的虚拟现实系统对设施维护规划至关重要。然而，这些设施精确的三维形状通常过于复杂，难以通过传统光栅化流水线以几何基元精确建模。本研究开发了Magic NeRF Lens——一个利用神经辐射场与体渲染技术支持沉浸式虚拟现实设施检测的交互框架。我们提出一种新颖的数据融合方法，融合体渲染与几何光栅化的互补优势，使NeRF模型能够与其他常规三维数据（如计算机辅助设计模型）合并。我们利用人类视觉特性与情境感知可视化原理，开发了两种新型三维魔镜效果以优化NeRF渲染。通过技术基准测试、视觉搜索用户实验及专家评估，验证了本框架与方法的高度可用性。此外，我们的VR NeRF框架源代码已公开，以供未来研究与开发使用。