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3D Reconstruction for Minimally Invasive Surgery: Lidar Versus Learning-Based Stereo Matching

2023

Miscellaneous

hi

This work investigates real-time 3D surface reconstruction for minimally invasive surgery. Specifically, we analyze depth sensing through laser-based time-of-flight sensing (lidar) and stereo endoscopy on ex-vivo porcine tissue samples. When compared to modern learning-based stereo matching from endoscopic images, lidar achieves lower processing delay, higher frame rate, and superior robustness against sensor distance and poor illumination. Furthermore, we report on the negative effect of near-infrared light penetration on the accuracy of time-of-flight measurements across different tissue types.

Author(s): Guido Caccianiga and Julian Nubert and Marco Hutter and Katherine J. Kuchenbecker.
Year: 2023
Month: May
Department(s): Haptische Intelligenz
Research Project(s): Intraoperative AR Assistance for Robot-Assisted Minimally Invasive Surgery
Bibtex Type: Miscellaneous (misc)
Paper Type: Workshop
Address: London, UK
How Published: Workshop paper (2 pages) presented at the ICRA Workshop on Robot-Assisted Medical Imaging
State: Published

BibTex 

@misc{Caccianiga23-ICRAWS-Lidar,
  title = {3{D} Reconstruction for Minimally Invasive Surgery: Lidar Versus Learning-Based Stereo Matching},
  author = {Caccianiga, Guido and Nubert, Julian and Hutter, Marco and Kuchenbecker., Katherine J.},
  howpublished = {Workshop paper (2 pages) presented at the ICRA Workshop on Robot-Assisted Medical Imaging},
  address = {London, UK},
  month = may,
  year = {2023},
  doi = {},
  month_numeric = {5}
}