Depth Map Upscaling Through Edge Weighted Optimization

Accurate depth maps are a pre-requisite in three-dimensional television, e.g. for high quality view synthesis, but this information is not always easily obtained. Depth information gained by correspondence matching from two or more views suffers from disocclusions and low-texturized regions, leading to erroneous depth maps. These errors can be avoided by using depth from dedicated range sensors, e.g. time-of-flight sensors. Because these sensors only have restricted resolution, the resulting depth data need to be adjusted to the resolution of the appropriate texture frame. Standard upscaling methods provide only limited quality results. This paper proposes a solution for upscaling low resolution depth data to match high resolution texture data. We introduce We introduce the Edge Weighted Optimization Concept (EWOC) for fusing low resolution depth maps with corresponding high resolution video frames by solving an overdetermined linear equation system. Similar to other approaches, we take information from the high resolution texture, but additionally validate this information with the low resolution depth to accentuate correlated data. Objective tests show an improvement in depth map quality in comparison to other upscaling approaches. This improvement is subjectively confirmed in the resulting view synthesis