Active Stereo Method for 3D Endoscopes using Deep-layer GCN and Graph Representation with Proximity Information

Techniques for 3D endoscopic systems have been widely studied for various reasons. Among them, active stereo based systems, in which structured-light patterns are projected to surfaces and endoscopic images of the pattern are analyzed to produce 3D depth images, are promising, because of robustness and simple system configurations. For those systems, finding correspondences between a projected pattern and an original pattern is an open problem. Recently, correspondence estimation by graph neural networks (GCN) using graph-based representation of the patterns were proposed for 3D endoscopic systems. One severe problem of the approach is that the graph matching by GCN is largely affected by the stability of the graph construction process using the detected patterns of a captured image. If the detected pattern is fragmented into small pieces, graph matching may fail and 3D shapes cannot be retrieved. In this paper, we propose a solution for those problems by applying deep-layered GCN and extended graph representations of the patterns, where proximity information is added. Experiments show that the proposed method outperformed the previous method in accuracies for correspondence matching for 3D reconstruction.