Image Guided Cost Aggregation for Hierarchical Depth Map Fusion

Estimating depth from a video sequence is still a challenging task in computer vision with numerous applications. Like other authors we utilize two major concepts developed in this field to achieve that task which are the hierarchical estimation of depth within an image pyramid as well as the fusion of depth maps from different views. We compare the application of various local matching methods within such a combined approach and can show the relative performance of local image guided methods in contrast to commonly used fixed–window aggregation. Since efficient implementations of these image guided methods exist and the available hardware is rapidly enhanced, the disadvantage of their more complex but also parallel computation vanishes and they will become feasible for more applications.