Real-time deformable SLAM with geometrically adapted template for dynamic monocular laparoscopic scenes

Intraoperative reconstruction of endoscopic scenes is a key technology for surgical navigation systems. The accuracy and efficiency of 3D reconstruction directly determine the effectiveness of navigation systems in a variety of clinical applications. While current deformable SLAM algorithms can meet real-time requirements, their underlying reliance on regular templates still makes it challenging to efficiently capture abrupt geometric features within scenes, such as organ contours and surgical margins. We propose a novel real-time monocular deformable SLAM algorithm with geometrically adapted template. To ensure real-time performance, the proposed algorithm consists of two threads: a deformation mapping thread updates the template at keyframe rate and a deformation tracking thread estimates the camera pose and the deformation at frame rate. To capture geometric features more efficiently, the algorithm first detects salient edge features using a pre-trained contour detection network and then constructs the template through a triangulation method with guidance of the salient features. We thoroughly evaluated this method on Mandala and Hamlyn datasets in terms of accuracy and performance. The results demonstrated that the proposed method achieves better accuracy with 0.75–7.95 https://github.com/Tang257/SLAM-with-geometrically-adapted-template .