Semantic-SuPer: A Semantic-aware Surgical Perception Framework for Endoscopic Tissue Identification, Reconstruction, and Tracking
This work addresses the need for robust surgical scene understanding in autonomous robotic surgery, representing an incremental advancement by combining existing semantic and geometric approaches.
The paper tackles the problem of accurate 3D perception in endoscopic surgery by integrating semantic information from image segmentation with geometric data, resulting in improved performance over baseline and state-of-the-art methods on challenging deforming tissue data.
Accurate and robust tracking and reconstruction of the surgical scene is a critical enabling technology toward autonomous robotic surgery. Existing algorithms for 3D perception in surgery mainly rely on geometric information, while we propose to also leverage semantic information inferred from the endoscopic video using image segmentation algorithms. In this paper, we present a novel, comprehensive surgical perception framework, Semantic-SuPer, that integrates geometric and semantic information to facilitate data association, 3D reconstruction, and tracking of endoscopic scenes, benefiting downstream tasks like surgical navigation. The proposed framework is demonstrated on challenging endoscopic data with deforming tissue, showing its advantages over our baseline and several other state-of the-art approaches. Our code and dataset are available at https://github.com/ucsdarclab/Python-SuPer.