  One of the major challenges in Minimally Invasive Surgery (MIS) such as
laparoscopy is the lack of depth perception. In recent years, laparoscopic
scene tracking and surface reconstruction has been a focus of investigation to
provide rich additional information to aid the surgical process and compensate
for the depth perception issue. However, robust 3D surface reconstruction and
augmented reality with depth perception on the reconstructed scene are yet to
be reported. This paper presents our work in this area. First, we adopt a
state-of-the-art visual simultaneous localization and mapping (SLAM) framework
- ORB-SLAM - and extend the algorithm for use in MIS scenes for reliable
endoscopic camera tracking and salient point mapping. We then develop a robust
global 3D surface reconstruction frame- work based on the sparse point clouds
extracted from the SLAM framework. Our approach is to combine an outlier
removal filter within a Moving Least Squares smoothing algorithm and then
employ Poisson surface reconstruction to obtain smooth surfaces from the
unstructured sparse point cloud. Our proposed method has been quantitatively
evaluated compared with ground-truth camera trajectories and the organ model
surface we used to render the synthetic simulation videos. In vivo laparoscopic
videos used in the tests have demonstrated the robustness and accuracy of our
proposed framework on both camera tracking and surface reconstruction,
illustrating the potential of our algorithm for depth augmentation and
depth-corrected augmented reality in MIS with monocular endoscopes.
