Increasing Efficiency in Disparity Calculation

In this paper a trade-off between the computation effort and the accuracy of the resulting disparity map, obtained using interpolation over spatial domain, is presented. The accuracy of the obtained disparity map is presented as the mean squared error calculated over the known disparity ground truth of test images, while efficiency increase is presented in terms of algorithm run-times. Even when reducing the search space for correspondences using epipolar geometry, disparity calculation methods are considered computationally more expensive than interpolation. We show that substantial efficiency increase can be gained using interpolation, in comparison to calculating the dense disparity map directly. As will be shown interpolation also permits us to approximate a disparity value for the occluded pixels. The main contribution of our work is the disparity calculation efficiency increase using interpolation, that fits the sparse disparity map as a 2D surface.