{
  "abstract": "Significance. Accurate intraoperative depth perception is important for autonomous and semi-autonomous robotic laparoscopic surgery. Conventional fringe projection profilometry can achieve millimeter-scale accuracy but often requires multi-shot acquisition, digital-micromirror-device projection, and projector-camera synchronization, complicating integration into compact laparoscopic systems. Aim. To develop a synchronization-free, single-shot depth-sensing platform using a passive LED-illuminated binary mask and a VQ-VAE prior with a custom U-Net depth head. Approach. A compact projection module was coupled to one channel of a dual-channel laparoscope, while the second channel imaged the fringe-illuminated target. A Zivid 3D camera acquired reference depth for 722 paired phantom images. Zivid depth maps were reprojected into the SSLE image frame for supervised training and evaluation. The VQ-VAE encoded each input into a discrete latent representation, and a latent-space U-Net predicted depth without a separate mask-prediction branch. Results. Using a fixed train/validation/test split, the proposed model achieved an MAE of 3.70 mm, AbsRel of 0.0326, delta=1.1 accuracy of 0.962, and delta=1.1^2 accuracy of 0.970. It achieved lower MAE than the dual U-Net MaskNet + DepthNet baseline and outperformed off-the-shelf monocular depth models in MAE, AbsRel, and threshold accuracy. The pipeline operated at 26.0 Hz over 301 consecutive frames on an NVIDIA A100 GPU. Conclusions. The LED-illuminated binary-pattern platform with latent-space depth reconstruction enables synchronization-free, video-rate endoscopic depth estimation. Results demonstrate Zivid-referenced phantom reconstruction without an explicit segmentation stage, while emphasizing the importance of dataset size and SSLE-Zivid calibration accuracy.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.05109",
  "author_total": 8,
  "authors": [
    {
      "name": "Wayne Wonseok Rodgers"
    },
    {
      "name": "Xiangyi Le"
    },
    {
      "name": "Seonghoon Jang"
    },
    {
      "name": "Shuwen Wei"
    },
    {
      "name": "Justin Opfermann"
    },
    {
      "name": "Michael Kam"
    },
    {
      "name": "Axel Krieger"
    },
    {
      "name": "Jin U. Kang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3n69my5scfmcn.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.05109",
  "fulltext_url": null,
  "id": "3n69my5scfmcn",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.05109",
  "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.RO"
    ],
    [
      "arxiv",
      "eess.IV"
    ],
    [
      "arxiv",
      "physics.optics"
    ],
    [
      "openalex",
      "1707|Optical measurement and interference techniques"
    ]
  ],
  "openalex": "W7196948820",
  "pdf_url": "https://arxiv.org/pdf/2608.05109",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-04",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3n69my5scfmcn.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:48Z",
      "native_id": "2608.05109",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.05109"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.optics",
      "subfield": "optics-photonics"
    }
  ],
  "title": "AI-based single-shot structured-light depth reconstruction for real-time laparoscopic surgical guidance",
  "title_text": "AI-based single-shot structured-light depth reconstruction for real-time laparoscopic surgical guidance",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:48Z",
  "url": "https://research.draconitic.com/paper/3n69my5scfmcn",
  "venue": "arXiv",
  "versions": []
}