  Tissue deformation during the surgery may significantly decrease the accuracy
of surgical navigation systems. In this paper, we propose an approach to
estimate the deformation of tissue surface from stereo videos in real-time,
which is capable of handling occlusion, smooth surface and fast deformation. We
first use a stereo matching method to extract depth information from stereo
video frames and generate the tissue template, and then estimate the
deformation of the obtained template by minimizing ICP, ORB feature matching
and as-rigid-as-possible (ARAP) costs. The main novelties are twofold: (1) Due
to non-rigid deformation, feature matching outliers are difficult to be removed
by traditional RANSAC methods; therefore we propose a novel 1-point RANSAC and
reweighting method to preselect matching inliers, which handles smooth surfaces
and fast deformations. (2) We propose a novel ARAP cost function based on dense
connections between the control points to achieve better smoothing performance
with limited number of iterations. Algorithms are designed and implemented for
GPU parallel computing. Experiments on ex- and in vivo data showed that this
approach works at an update rate of 15Hz with an accuracy of less than 2.5 mm
on a NVIDIA Titan X GPU.
