  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.
