{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.23425",
  "author_total": 6,
  "authors": [
    {
      "name": "Dhruv Gamdha"
    },
    {
      "name": "James Afful"
    },
    {
      "name": "Shambhavi Joshi"
    },
    {
      "name": "Ulrike Passe"
    },
    {
      "name": "Adarsh Krishnamurthy"
    },
    {
      "name": "Baskar Ganapathysubramanian"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/asszxvcthppx3.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.23425",
  "fulltext_url": null,
  "id": "asszxvcthppx3",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.23425",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.CE"
    ],
    [
      "arxiv",
      "cs.CV"
    ],
    [
      "arxiv",
      "physics.flu-dyn"
    ],
    [
      "openalex",
      "2206|3D Shape Modeling and Analysis"
    ]
  ],
  "openalex": "W7214008819",
  "pdf_url": "https://arxiv.org/pdf/2609.23425",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/asszxvcthppx3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:19Z",
      "native_id": "2609.23425",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.23425"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.flu-dyn",
      "subfield": "fluid-dynamics"
    }
  ],
  "title": "Semi-automated reconstruction of indoor geometry from 360-degree video for CFD-based airflow analysis in classrooms",
  "title_text": "Semi-automated reconstruction of indoor geometry from 360-degree video for CFD-based airflow analysis in classrooms",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:19Z",
  "url": "https://research.draconitic.com/paper/asszxvcthppx3",
  "venue": "arXiv",
  "versions": []
}