基于360度视频的半自动室内几何重建用于教室CFD气流分析
Semi-automated reconstruction of indoor geometry from 360-degree video for CFD-based airflow analysis in classrooms
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中文总结 AI 辅助
提出一种半自动工作流程，从360度视频通过NeRF、SAM 3分割和ICP配准重建教室可编辑几何，用于CFD通风分析，实现低成本逐房间建模并验证清除时间不可跨房间外推。
中文摘要 AI 辅助
计算流体动力学（CFD）被广泛用于评估有人建筑内的通风和污染物输运，但其大规模部署受限于三个瓶颈：在无需昂贵扫描硬件或手动CAD建模的情况下获取房间几何结构、将场景分解为可单独操作的物体、以及在不重新捕获房间的情况下为替代布局重新配置这些物体。我们提出了一种半自动工作流程，可将房间的单段360度视频转换为可单独编辑、可直接用于仿真的几何资产。利用神经辐射场（NeRF）重建稠密点云，并将来自文本提示的SAM 3分割的2D实例掩码通过多视图一致性和深度带滤波提升至3D。点通过八叉树分离为物体实例，遮挡间隙通过连通图修复。椅子模板通过迭代最近点（ICP）配准进行拟合，桌子几何则程序化生成。基于浏览器的编辑器支持质量保证和替代布局配置的快速构建。稳态雷诺平均OpenFOAM解随后驱动瞬态被动标量输运；该设置通过网格敏感性研究验证，并对照IEA Annex 20基准进行验证。我们将该工作流程应用于两间大学教室和一个阶梯式讲堂报告厅。在消费级工作站上，每间房间的从捕获到几何的处理耗时两到五小时。在其中一间教室的受控遮挡序列中，建模的半清除时间随家具添加呈非单调变化，跨房间比较表明清除行为无法在房间之间可靠外推，这促使需要逐房间获取几何。通过使该获取低成本化，该工作流程使几何分辨的比较通风研究对教室等空间变得实用。
英文摘要
Computational Fluid Dynamics (CFD) is widely used to evaluate ventilation and contaminant transport in occupied buildings, but deployment at scale is limited by three bottlenecks: acquiring room geometry without costly scanning hardware or manual CAD modeling, decomposing the scene into individually manipulable objects, and reconfiguring those objects for alternative layouts without re-capturing the room. We present a semi-automated workflow that converts a single 360-degree video of a room into individually editable, simulation-ready geometry assets. A dense point cloud is reconstructed using Neural Radiance Fields (NeRF), and 2D instance masks from text-prompted SAM 3 segmentation are lifted to 3D using multi-view consensus and depth-band filtering. Points are separated into object instances with an octree, and occlusion gaps are healed with a connectivity graph. Chair templates are fitted by Iterative Closest Point (ICP) alignment, and table geometry is generated procedurally. A browser-based editor supports quality assurance and rapid construction of alternative layout configurations. A steady Reynolds-averaged OpenFOAM solution then drives transient passive-scalar transport; the setup is verified using a mesh-sensitivity study and validated against an IEA Annex 20 benchmark. We apply the workflow to two university classrooms and a tiered lecture-hall auditorium. The capture-to-geometry pass takes two to five hours per room on a consumer workstation. In a controlled obstruction sequence in one classroom, the modeled half-clearance time varies non-monotonically as furniture is added, and a cross-room comparison indicates that clearance behavior cannot be reliably extrapolated between rooms, motivating per-room geometry acquisition. By making that acquisition low-cost, the workflow makes geometry-resolved comparative ventilation studies practical for spaces such as classrooms.
发表机构
- Iowa State University（爱荷华州立大学）
机构由 AI 辅助整理，请以论文原文为准。
