Depth estimation based on multiview matching with depth/color segmentation and memory efficient Belief Propagation

3D technologies are becoming the more and more relevant in recent years. Visual communications, as well as image and video analysis, benefit in great manner from spatial information such as depth for various applications. Highly accurate visual depth estimation often involves complex optimization algorithms in order to fit proper estimation models to data. From a stereo/multiview matching perspective, local and global algorithms exist. Commonly, the latter are more complex and accurate, as data models are used to take the global structure into account. Belief Propagation has proven to be a good global algorithmic framework for depth estimation. By means of an iterative procedure, it is able to regularize, according to set of local smoothness and geometry constrains, an initial estimation of depth by a local approach such as simple block matching. However, information transfer from iteration to iteration by means of message passing can be excessively demanding in terms of memory bandwidth and usage. In this paper, a new Belief Propagation based algorithm with multiview matching with depth/color segmentation is proposed together with a strategy for message passing compression. Experimental results show the algorithm to be competitive with best performing ones in the state of the art, while reducing by a factor 10 the memory usage, with marginal loss in performance, of a typical Belief Propagation strategy.