{
  "abstract": "Bubble size, depth, and spatial distribution govern transport and uniformity in multiphase flows. A single shadowgraphic view cannot separate bubble diameter from focal-plane displacement, as image signatures depend on both, while multicamera systems add optical access, calibration, and cost. We develop a single-camera physics-guided framework combining direct numerical simulation (DNS), validated Monte Carlo ray tracing, preprocessing, and focus-conditioned multitask deep learning to recover three-dimensional bubble position and physical-equivalent diameter from two-dimensional shadowgraphs. DNS-resolved bubble label images rendered at multiple focal-plane settings. The ray tracer reproduces shadowgraphic signatures with errors below $3\\%$ (intensity), $1\\%$ (apparent size), $15\\%$ (blur), $6\\%$ (numerical aperture), and $4\\%$ (edge-spread-function shape), running $\\sim30\\times$ faster than the CPU implementation. The dataset has $900$ frames and $87{,}728$ labeled instances, partitioned by DNS timestamp to prevent leakage and expanded to over $10^{5}$ crops by broad-depth controlled-density rendering. A multitask network predicts signed depth and diameter from crops and focal-plane coordinates, achieving $\\sim97.6\\%$ normalized depth accuracy on held-out data; a closed-loop DNS-to-image-to-reconstruction test of $180$ bubbles gives diameter error below $9.71\\%$ ($\\sim90.29\\%$ accuracy). On experimental images at four radial planes ($142$--$971$ bubbles each), PDF overlap is $68\\%$--$92\\%$ and log-PDF correlation is $0.72$--$0.87$. At the focal reference plane ($11{,}679$ bubbles), overlap is $\\sim86\\%$, correlation is $0.93$, and full-distribution, $D_{99.8}$, and composite errors are below $7.7\\%$, increasing with focal-plane distance and defining a recoverability limit. Without stereoscopic hardware, the framework offers a scalable diagnostic for dense bubbly flows.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.28908",
  "author_total": 6,
  "authors": [
    {
      "name": "Chaitanya S. Nayak"
    },
    {
      "name": "Faizaan Mohammed"
    },
    {
      "name": "Vivek Kumar"
    },
    {
      "name": "Shivam Prajapati"
    },
    {
      "name": "Suhas S. Jain"
    },
    {
      "name": "Cyrus K. Aidun"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ap2r55crmsqd9.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.28908",
  "fulltext_url": null,
  "id": "ap2r55crmsqd9",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.28908",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.flu-dyn"
    ],
    [
      "openalex",
      "1707|Advanced Vision and Imaging"
    ]
  ],
  "openalex": "W7223225179",
  "pdf_url": "https://arxiv.org/pdf/2610.28908",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ap2r55crmsqd9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:34:19Z",
      "native_id": "2610.28908",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.28908"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.flu-dyn",
      "subfield": "fluid-dynamics"
    }
  ],
  "title": "Monocular 3D bubble reconstruction: DNS-informed ray tracing and deep learning",
  "title_text": "Monocular 3D bubble reconstruction: DNS-informed ray tracing and deep learning",
  "type": "preprint",
  "updated_at": "2026-10-30T12:34:19Z",
  "url": "https://research.draconitic.com/paper/ap2r55crmsqd9",
  "venue": "arXiv",
  "versions": []
}