{
  "abstract": "Indirect time-of-flight (iToF) cameras provide compact and cost-effective dense depth measurements, but their ranging accuracy is often degraded by sensor-intrinsic uncertainty under practical imaging conditions. Spatially uniform or range-only Gaussian perturbations cannot accurately reproduce the range-dependent and signal-dependent noise characteristics of real iToF measurements, leading to a synthetic-to-real gap for learning-based restoration. To address this problem, we propose a joint depth-uncertainty modeling and restoration framework for reliable iToF sensing. A sensor-intrinsic depth-uncertainty model is first developed from calibrated tap responses, returned-signal levels, and sensor noise statistics through a depth-oriented weighted least-squares formulation. The resulting pixel-wise uncertainty is used for heteroscedastic depth synthesis and uncertainty-aware restoration supervision. Based on this heteroscedastic data synthesis, we further develop a U-shaped restoration network with Depth Visual State Space (DVSS) blocks, which combine long-range state-space modeling with convolutional spatial-channel refinement for structure-preserving depth recovery. Experiments on synthetic data and measurements captured by an in-house iToF prototype validate the proposed uncertainty model under varying range and returned-signal conditions. Controlled comparisons with fixed and range-aware Gaussian noise, together with evaluations on U-Net, Restormer, and DVSS, further demonstrate that the proposed synthesis consistently benefits different restoration backbones. The complete framework achieves 40.85~dB PSNR and 2.54 mm MAE on the synthetic test set, and 35.42 dB PSNR and 4.87 mm MAE on real iToF measurements.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.05507",
  "author_total": 10,
  "authors": [
    {
      "name": "Yansong Du"
    },
    {
      "name": "Yutong Deng"
    },
    {
      "name": "Yuting Zhou"
    },
    {
      "name": "Zhancong Xu"
    },
    {
      "name": "Yingjia Lu"
    },
    {
      "name": "Mengdi Wang"
    },
    {
      "name": "Feiyu Jiao"
    },
    {
      "name": "Bangyao Wang"
    },
    {
      "name": "Zhaoxiang Jiang"
    },
    {
      "name": "Xun Guan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a1zmanj5d1jpk.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.05507",
  "fulltext_url": null,
  "id": "a1zmanj5d1jpk",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.05507",
  "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",
      "cs.CV"
    ],
    [
      "arxiv",
      "physics.optics"
    ],
    [
      "openalex",
      "3105|Advanced Optical Sensing Technologies"
    ]
  ],
  "openalex": "W7211991838",
  "pdf_url": "https://arxiv.org/pdf/2609.05507",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/a1zmanj5d1jpk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:48Z",
      "native_id": "2609.05507",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.05507"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.optics",
      "subfield": "optics-photonics"
    }
  ],
  "title": "Reliable iToF Depth Sensing via Sensor-Intrinsic Uncertainty Modeling and State-Space Restoration",
  "title_text": "Reliable iToF Depth Sensing via Sensor-Intrinsic Uncertainty Modeling and State-Space Restoration",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:48Z",
  "url": "https://research.draconitic.com/paper/a1zmanj5d1jpk",
  "venue": "arXiv",
  "versions": []
}