Uncalibrated stereo rectification and disparity range stabilization: a comparison of different feature detectors

Abstract

This paper studies uncalibrated stereo rectification and stable disparity range determination for surgical scene three-dimensional (3-D) reconstruction. Stereoscopic endoscope calibration sometimes is not available and also increases the complexity of the operating-room environment. Stereo from uncalibrated endoscopic cameras is an alternative to reconstruct the surgical field visualized by binocular endoscopes within the body. Uncalibrated rectification is usually performed on the basis of a number of matched feature points (semi-dense correspondence) between the left and the right images of stereo pairs. After uncalibrated rectification, the corresponding feature points can be used to determine the proper disparity range that helps to improve the reconstruction accuracy and reduce the computational time of disparity map estimation. Therefore, the corresponding or matching accuracy and robustness of feature point descriptors is important to surgical field 3-D reconstruction. This work compares four feature detectors: (1) scale invariant feature transform (SIFT), (2) speeded up robust features (SURF), (3) affine scale invariant feature transform (ASIFT), and (4) gauge speeded up robust features (GSURF) with applications to uncalibrated rectification and stable disparity range determination. We performed our experiments on surgical endoscopic video images that were collected during robotic prostatectomy. The experimental results demonstrate that ASIFT outperforms other feature detectors in the uncalibrated stereo rectification and also provides a stable stable disparity range for surgical scene reconstruction.