  Reconstructing the 3D shape of a deformable environment from the information
captured by a moving depth camera is highly relevant to surgery. The underlying
challenge is the fact that simultaneously estimating camera motion and tissue
deformation in a fully deformable scene is an ill-posed problem, especially
from a single arbitrarily moving viewpoint. Current solutions are often
organ-specific and lack the robustness required to handle large deformations.
Here we propose a multi-viewpoint global optimization framework that can
flexibly integrate the output of low-level perception modules (data
association, depth, and relative scene flow) with kinematic and scene-modeling
priors to jointly estimate multiple camera motions and absolute scene flow. We
use simulated noisy data to show three practical examples that successfully
constrain the convergence to a unique solution. Overall, our method shows
robustness to combined noisy input measures and can process hundreds of points
in a few milliseconds. MultiViPerFrOG builds a generalized learning-free
scaffolding for spatio-temporal encoding that can unlock advanced surgical
scene representations and will facilitate the development of the
computer-assisted-surgery technologies of the future.
