# PERSEUS: Perception with Semantic Endoscopic Understanding and SLAM Source: [PERSEUS: Perception with Semantic Endoscopic Understanding and SLAM]() Collection index: [llms.txt]() Data updated: 2026-01-22T05:00:21.476Z Authors: Ayberk Acar, Fangjie Li, Susheela Sharma Stern, Lidia Al-Zogbi, Hao Li, Kanyifeechukwu Jane Oguine, Dilara Isik, Brendan Burkhart, Jesse F. d'Almeida, Robert J. Webster III, Ipek Oguz, Jie Ying Wu Published: 2025-09-16T21:14:16.000Z ## Abstract Purpose: Natural orifice surgeries minimize the need for incisions and reduce the recovery time compared to open surgery; however, they require a higher level of expertise due to visualization and orientation challenges. We propose a perception pipeline for these surgeries that allows semantic scene understanding. Methods: We bring learning-based segmentation, depth estimation, and 3D reconstruction modules together to create real-time segmented maps of the surgical scenes. Additionally, we use registration with robot poses to solve the scale ambiguity of mapping from monocular images, and allow the use of semantically informed real-time reconstructions in robotic surgeries. Results: We achieve sub-milimeter reconstruction accuracy based on average one-sided Chamfer distances, average pose registration RMSE of 0.9 mm, and an estimated scale within 2% of ground truth. Conclusion: We present a modular perception pipeline, integrating semantic segmentation with real-time monocular SLAM for natural orifice surgeries. This pipeline offers a promising solution for scene understanding that can facilitate automation or surgeon guidance. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()