Obtaining depth map from segment-based stereo matching using graph cuts

TL;DR: A hierarchical bilateral disparity structure (HBDS) algorithm in which the efficiency of the GC method is improved without any loss in the disparity estimation performance by dividing all the disparity levels within the stereo image hierarchically into a series of bilateral disparity structures of increasing fineness is proposed.

TL;DR: A segmentation-based stereo matching algorithm using an adaptive multi-cost approach, which is exploited for obtaining accuracy disparity maps and the experimental results with the Middlebury stereo datasets, along with synthesized and real-world stereo images, demonstrate the effectiveness of the proposed approach.

TL;DR: A stereo framework that views the scene as a set of 3D entities with compact and smooth disparity distributions with a joint second-order smoothness prior, which combines a geometric weight with the derivative of disparity values to encourage smooth disparity variations inside each entity.

TL;DR: The results indicate that the proposed FastNL_Gc method is compatible with the current state-of-the-art stereo matching algorithms in dealing with the conventionally difficult areas, such as textureless regions, disparity discontinuous boundaries and occluded portions.

TL;DR: This paper has designed a stand-alone, flexible C++ implementation that enables the evaluation of individual components and that can easily be extended to include new algorithms.

TL;DR: This work presents two algorithms based on graph cuts that efficiently find a local minimum with respect to two types of large moves, namely expansion moves and swap moves that allow important cases of discontinuity preserving energies.

TL;DR: This work gives a precise characterization of what energy functions can be minimized using graph cuts, among the energy functions that can be written as a sum of terms containing three or fewer binary variables.

TL;DR: Algorithmic techniques are presented that substantially improve the running time of the loopy belief propagation approach and reduce the complexity of the inference algorithm to be linear rather than quadratic in the number of possible labels for each pixel, which is important for problems such as image restoration that have a large label set.