Dense Depth Estimation for Monocular Endoscope Robot with an Adaptive Baseline

Depth information is useful to surgeons and surgical assistance systems. However, it is a challenging task to estimate the depth of various surgical scenes based on a monocular endoscope. We propose a depth estimation approach for a monocular endoscope with a stereo matching algorithm. The monocular endoscope is moved horizontally by a robotic endoscope holder to simulate a stereo vision system. The main challenge is how to obtain a proper baseline for better depth information generation as the depth range of a surgical scene is unknown beforehand. We design a baseline evaluation and selection algorithm to search for suitable baselines for surgical scenes with different depth ranges. Experimental results show that our approach improves the average accuracy of different depth scenarios by 10.8% when the error range is 2mm.