SLAM based Quasi Dense Reconstruction For Minimally Invasive Surgery Scenes
This addresses the need for surgical guidance and augmented reality in minimally invasive surgery, representing an incremental improvement by adapting existing SLAM methods to this domain.
The paper tackled the problem of reconstructing surgical scene structure in laparoscopy by proposing a quasi-dense reconstruction algorithm based on a state-of-the-art SLAM system, achieving a Root Mean Squared Error of 4.9mm in validation with a live porcine experiment.
Recovering surgical scene structure in laparoscope surgery is crucial step for surgical guidance and augmented reality applications. In this paper, a quasi dense reconstruction algorithm of surgical scene is proposed. This is based on a state-of-the-art SLAM system, and is exploiting the initial exploration phase that is typically performed by the surgeon at the beginning of the surgery. We show how to convert the sparse SLAM map to a quasi dense scene reconstruction, using pairs of keyframe images and correlation-based featureless patch matching. We have validated the approach with a live porcine experiment using Computed Tomography as ground truth, yielding a Root Mean Squared Error of 4.9mm.