Robust Cost Volume Generation Method for Dense Stereo Matching in Endoscopic Scenarios.

Stereo matching in binocular endoscopic scenarios is difficult due to the radiometric distortion caused by restricted light conditions. Traditional matching algorithms suffer from poor performance in challenging areas, while deep learning ones are limited by their generalizability and complexity. We introduce a non-deep learning cost volume generation method whose performance is close to a deep learning algorithm, but with far less computation. To deal with the radiometric distortion problem, the initial cost volume is constructed using two radiometric invariant cost metrics, the histogram of gradient angle and amplitude descriptors. Then we propose a new cross-scale propagation framework to improve the matching reliability in small homogenous regions without increasing the running time. The experimental results on the Middlebury Version 3 Benchmark show that the performance of the combination of our method and Local-Expansion, an optimization algorithm, ranks top among non-deep learning algorithms. Other quantitative experimental results on a surgical endoscopic dataset and our binocular endoscope show that the accuracy of the proposed algorithm is at the millimeter level which is comparable to the accuracy of deep learning algorithms. In addition, our method is 65 times faster than its deep learning counterpart in terms of cost volume generation.