Joint view synthesis and disparity refinement for stereo matching

Typical stereo algorithms treat disparity estimation and view synthesis as two sequential procedures.In this paper,we consider stereo matching and view synthesis as two complementary components,and present a novel iterative refinement model for joint view synthesis and disparity refinement.To achieve the mutual promotion between view synthesis and disparity refinement,we apply two key strategies,disparity maps fusion and disparity-assisted plane sweep-based rendering(DAPSR).On the one hand,the disparity maps fusion strategy is applied to generate disparity map from synthesized view and input views.This strategy is able to detect and counteract disparity errors caused by potential artifacts from synthesized view.On the other hand,the DAPSR is used for view synthesis and updating,and is able to weaken the interpolation errors caused by outliers in the disparity maps.Experiments on Middlebury benchmarks demonstrate that by introducing the synthesized view,disparity errors due to large occluded region and large baseline are eliminated effectively and the synthesis quality is greatly improved.

Depth Map Prediction of Occluded Objects Using Structure Tensor with Gain Regularization

The creation of the 3D rendering model involves the prediction of an accurate depth map for the input images.A proposed approach of a modified semi-global block matching algorithm with variable window size and the gradient assessment of objects predicts the depth map.3D modeling and view synthesis algorithms could effectively handle the obtained disparity maps.This work uses the consistency check method to find an accurate depth map for identifying occluded pixels.The prediction of the disparity map by semi-global block matching has used the benchmark dataset of Middlebury stereo for evaluation.The improved depth map quality within a reasonable process-ing time outperforms the other existing depth map prediction algorithms.The experimental results have shown that the proposed depth map predictioncould identify the inter-object boundaryeven with the presence ofocclusion with less detection error and runtime.We observed that the Middlebury stereo dataset has very few images with occluded objects,which made the attainment of gain cumbersome.Considering this gain,we have created our dataset with occlu-sion using the structured lighting technique.The proposed regularization term as an optimization process in the graph cut algorithm handles occlusion for different smoothing coefficients.The experimented results demonstrated that our dataset had outperformed the Tsukuba dataset regarding the percentage of occluded pixels.

Local algorithms on dense two-frame stereo matching

Stereo matching is a fundamental and crucial problem in computer vision. In the last decades, many researchers have been working on it and made great progress. Generally stereo algorithms can be classified into local methods and global methods. In this paper, the challenges of stereo matching are first introduced, and then we focus on local approaches which have simpler structures and higher efficiency than global ones. Local algorithms generally perform four steps： cost computation, cost aggregation, disparity computation and disparity refinement. Every step is deeply investigated, and most work focuses on cost aggregation. We studied most of the classical local methods and divide them into several classes. The classification well illustrates the development history of local stereo correspondence and shows the essence of local matching along with its important and difficult points. At the end we give the future development trend of local methods.