Problem
Research questions and friction points this paper is trying to address.
Computational Fluid Dynamics
room geometry
360-degree video
indoor reconstruction
airflow analysis
Semi-automated reconstruction of indoor geometry from 360-degree video for CFD-based airflow analysis in classrooms
🤖 AI Summary
本文提出一种半自动化流程，通过360度视频重建室内几何结构，以解决CFD分析中获取房间几何形状和物体布局的问题，采用NeRF和SAM 3等技术。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Neural Radiance Fields (NeRF)
text-prompted SAM 3 segmentation
multi-view consensus
depth-band filtering
Iterative Closest Point (ICP)
🔎 Similar Papers
No similar papers found.
