Fully Auto-Calibrated Active-Stereo-Based 3d Endoscopic System Using Correspondence Estimation With Graph Convolutional Network

We have developed a series of 3D endoscopic systems where a micro-sized pattern projector is inserted through the instrument channel of the endoscope and shapes are reconstructed by a structured light technique using captured images of the endoscopic camera. One problem of the previous works is that the accuracy of shape reconstruction is low, because the projector cannot be fixed to the endoscope, and thus, the pose of the pattern projector w.r.t. the camera cannot be pre-calibrated. In this paper, we propose a method to auto-calibrate the pose of the projector without using any special devices nor manual process. Since the technique is one-shot, multiple shapes can be reconstructed from an image sequence and a large 3D scene can be recovered by merging them. Experiments are conducted using the real system.