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3D Reconstruction for Minimally Invasive Surgery: Lidar Versus Learning-Based Stereo Matching
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.
@misc{Caccianiga23-ICRAWS-Lidar, title = {3{D} Reconstruction for Minimally Invasive Surgery: Lidar Versus Learning-Based Stereo Matching}, abstract = {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.}, howpublished = {Workshop paper (2 pages) presented at the ICRA Workshop on Robot-Assisted Medical Imaging}, address = {London, UK}, month = may, year = {2023}, slug = {caccianiga23-icraws-lidar}, author = {Caccianiga, Guido and Nubert, Julian and Hutter, Marco and Kuchenbecker., Katherine J.}, month_numeric = {5} }