  Models for near-rigid shape matching are typically based on distance-related
features, in order to infer matches that are consistent with the isometric
assumption. However, real shapes from image datasets, even when expected to be
related by "almost isometric" transformations, are actually subject not only to
noise but also, to some limited degree, to variations in appearance and scale.
In this paper, we introduce a graphical model that parameterises appearance,
distance, and angle features and we learn all of the involved parameters via
structured prediction. The outcome is a model for near-rigid shape matching
which is robust in the sense that it is able to capture the possibly limited
but still important scale and appearance variations. Our experimental results
reveal substantial improvements upon recent successful models, while
maintaining similar running times.
