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Segmentation-based Method of Increasing The Depth Maps Temporal Consistency

Mieloch D., Grzelka A.

International Journal of Electronics and Telecommunications

2018

Vol. 64, No. 3

293--298

In this paper, a modification of the graph-based depth estimation is presented. The purpose of proposed modification is to increase the quality of estimated depth maps, reduce the time of the estimation, and increase the temporal consistency of depth maps. The modification is based on the image segmentation using superpixels, therefore in the first step of the proposed modification a segmentation of previous frames is used in the currently processed frame in order to reduce the overall time of the depth estimation. In the next step, a depth map from the previous frame is used in the depth map optimization as the initial values of a depth map estimated for the current frame. It results in the better representation of silhouettes of objects in depth maps and in the reduced computational complexity of the depth estimation process. In order to evaluate the performance of the proposed modification the authors performed the experiment for a set of multiview test sequences that varied in their content and an arrangement of cameras. The results of the experiments confirmed the increase of the depth maps quality - the quality of depth maps calculated with the proposed modification is higher than for the unmodified depth estimation method, apart from the number of the performed optimization cycles. Therefore, use of the proposed modification allows to estimate a depth of the better quality with almost 40% reduction of the estimation time. Moreover, the temporal consistency, measured through the reduction of the bitrate of encoded virtual views, was also considerably increased.

Using graphical models for depth map estimation

Kozik R., Marchewka A.

Image Processing & Communications

2011

Vol. 16, no 3-4

5-12

Two slightly different projections of the same scene allow the stereovision algorithms to reconstruct its 3D structure and to estimate the distance to particular object. However commonly used real-time correlation-based solutions usually suffer from inaccuracy. Therefore, finding an efficient and accurate algorithm for depth reconstruction is still a challenging task to do. The approach to stereo matching, presented in this paper is described as a problem of correlating different local observations that evaluate the dissimilarities between left and right images in order to obtain smooth and more accurate depth map. The results obtained with the proposed method are evaluated and compared with other state of the art methods.