Improved best match search method in depth recovery with descent images
Searching for the best match is a key step in stereovision or 3D reconstruction with images. In our previous study, we recovered every pixels depth in the lower image by searching for the highest correlation in its dimensional ZNCC correlation curve in the cubic correlation matrix as the best match, but sparse reconstruction errors such as holes or spikes existed in the result. After careful analysis we found that not all the highest-correlational positions are corresponding to their correct match due to noise or similarity. Therefore, a new best match search method based on best seed propagation first strategy is proposed by considering neighborhood disparity constraints. At first, some pixels are chosen as initial seeds and inserted into a seed queue by assessing their correlation curves. Their depths are determined by the layers in the cubic correlation matrix in which they get their highest correlation value. Second, the front seed is taken out of the queue and its neighbor points are propagated as new seeds under the propagation rules. The new propagated seeds will also be inserted into the seed queue, and their depth are accordingly decided. This operation is repeated till the seed queue is null. At last, there will be some points which are never propagated as seeds according to the propagation rules. Their depths are determined by their neighbor points depth information through post processing. The comparison experiments show that the new method can improve the accuracy of the matches and reduce the reconstruction error effectively.
best match search depth recovery descent images 3d reconstruction best seed propagation first