arXiv:2608.05109v2 Announce Type: replace-cross 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.
Innovación tecnológica y arquitectura del sistema
Focus to learn more arXiv-issued DOI via DataCite Submission history From: Wayne (Wonseok) Rodgers (Choi) [ view email ] [v1] Wed, 5 Aug 2026 17:44:21 UTC (16,878 KB) [v2] Fri, 4 Sep 2026 18:29:59 UTC (16,023 KB) Full-text links: Access Paper: View a PDF of the paper titled AI-based single-shot structured-light depth reconstruction for real-time laparoscopic surgical guidance, by Wayne Wonseok Rodgers and 7 other authors View PDF HTML (experimental) TeX Source view license Current browse context: eess.IV new | recent | 2026-08 Change to browse by: cs cs.RO eess physics physics.optics References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
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- Percepción y control dinámico: Integración de visión por computadora y sensores hápticos con tiempos de latencia ultra reducidos.
- Generalización de tareas: Capacidad de transferir habilidades aprendidas en simulación hacia entornos no estructurados del mundo real.
- Eficiencia operativa: Reducción en los tiempos de ciclo y consumo energético optimizado en tareas repetitivas de alta demanda.
"Lo vi en AI Robot: La convergencia entre modelos de inteligencia artificial corpórea y maquinaria física de precisión está acelerando la transición de la teoría de laboratorio a la producción industrial a gran escala."
Impacto en la industria, ROI y cadena de valor
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Perspectivas de despliegue y visión hacia el futuro
La continua maduración de estos desarrollos evidencia que el futuro de la automatización no reside únicamente en réplicas de forma humana, sino en la dotación de autonomía cognitiva a toda clase de plataformas móviles, brazos manipuladores y vehículos industriales.
🔗 Recursos y Enlaces Recomendados para Profundizar
- ArXiv Computer Science: Robotics (cs.RO) ↗ — Pre-prints e investigaciones académicas de frontera en robótica.
- Hugging Face Robotics (LeRobot) ↗ — Modelos abiertos, datasets y código para aprendizaje por imitación y VLA.
