AI 中文总结
本文提出SceneBaker程序化场景生成流水线，无需GUI即可将地图和地形数据转换为Sionna RT就绪的Mitsuba场景，在四个校园场景中生成的场景可避免典型错误，且信道响应与Blender基线相当。
AI 中文摘要
无线数字孪生（DT）研究需要可生成、可版本化且可重复检查的射线追踪（RT）场景。当前的可视化创作工作流可生成逼真的城市模型，但与重复的无线电模拟研究匹配度差，因为几何结构、地形接触和材料语义在导出后常需手动修复。本文提出SceneBaker，一种程序化场景生成流水线，可将地图和地形数据转换为Sionna RT就绪的Mitsuba场景，无需GUI创作步骤。在四个校园场景中，SceneBaker生成的Sionna RT就绪场景与Blender生成的基线场景地理范围相同，生成场景避免了典型的建筑生成和地形接触错误，同时保留了可比的覆盖范围场和链路级信道响应行为。实现代码和生成的对比资产可在此https URL获取。
英文摘要
Wireless digital-twin (DT) research needs ray-tracing (RT) scenes that can be generated, versioned, and checked reproducibly. Current visual-authoring workflows can produce plausible city models, but they are poorly matched to repeated radio-simulation studies because geometry, terrain contact, and material semantics often require manual repair after export. This paper presents SceneBaker, a programmatic scene-generation pipeline that turns map and terrain data into Sionna RT-ready Mitsuba scenes without a GUI authoring step. Across four campus scenes, SceneBaker generates Sionna RT-ready scenes over the same geographic bounds as a Blender-generated baseline. The generated scenes avoid representative building-generation and terrain-contact faults while preserving comparable coverage-field and link-level channel-response behavior. The implementation and generated comparison assets are available at https://github.com/hslyu/sionna-scene-baker.
