Stereo depth estimation using synchronous optimization with segment based regularization

Stereo correspondence is inherently an ill-posed problem, which is addressed by regularization methods. This paper introduces a novel stereo correspondence method that uses two synchronous interdependent optimizations. The regularization of the correspondence problem is done adaptively by considering the image segments and the intermediate disparity maps of the two optimizations. Our adaptive regularization allows inter-segment diffusion at the beginning of the optimizations to be robust against local minima. When the two optimizations start producing similar disparity maps, our regularization prevents inter-segment diffusion to recover the depth discontinuities. Our experimental results showed that the proposed algorithm can handle sharp discontinuities well and provides disparity maps with accuracy comparable to the state of the art stereo methods.

Research highlights
► The system uses two energy functionals which are minimized synchronously by two dependent optimizations.
► It also introduced a novel adaptive regularizer which efficiently utilizes segmented version of the image and the positional differences between these optimizations.
► The system addresses many problems common to approximate optimization methods, such as sensitivity to initializations and selecting optimal stopping criteria.