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We propose a surgical navigation system aimed at conducting Depth–Depth Matching (DDM) between virtual and real organ images. The depth image of virtual organs modeled using stereolithography data derived from the Zbuffer of a GPU. In contrast, the depth image of real organs is obtained through an arbitrary depth camera. Therefore, in DDM, we need only non-combinatorial L subtractions and additions between virtual and real 2D depth images with pixel number of L, which is approximately 100,000. The most popular Iterative Closest Point (ICP) algorithm in the point cloud library consumes a considerable amount of time for checking the coincidence of two kinds of point clouds of whole organs. This could be because (1) the ICP needs combinatorial M × N calculation of the Euclidean distances of 3D cloud points (where M and N are usually near 100,000) and (2) considering that a real organ is obstructed by the patient’s body, the directions from which it is captured by a camera are restricted to the top view or near a shadowless lamp.

Depth–Depth Matching of Virtual and Real Images for a Surgical Navigation System