Non-Rigid Shape Correspondence Between Local and Global Scale
In this talk I will give an overview about the state of non-rigid correspondence methods in the era of deep learning, where current methods shine and what limitations still exist today. These range from the question whether a found solution is actually the global optimum, down to how fine-grained details in shapes from different classes can be matched to each other. Finally, we will look at the case of partial-to-partial correspondence in which no full mapping exists from one shape to another and the additional problem of estimating the overlaps poses significant challenges.
