Niloy J. Mitra, Leonidas Guibas, Joachim Giesen and Mark Pauly, Probabilistic Fingerprints for Shapes,
Symposium on Geometry Processing, pp. 121-130, 2006.
We propose a new probabilistic framework for the efficient estimation of similarity between 3D shapes. Our framework is based on local shape signatures and is designed to allow for quick pruning of dissimilar
shapes, while guaranteeing not to miss any shape with significant similarities to the query model in shape database retrieval applications. Since directly evaluating 3D similarity for large collections of signatures on shapes is expensive and impractical, we propose a suitable but compact approximation based on probabilistic fingerprints which are computed from the shape signatures using Rabinís hashing scheme and a small set of random permutations. We provide a probabilistic analysis that shows that
while the preprocessing time depends on the complexity of the model, the fingerprint size and hence the query time depends only on the desired confidence in our estimated similarity. Our method is robust to
noise, invariant to rigid transforms, handles articulated deformations, and effectively detects partial matches. In addition, it provides important hints about correspondences across shapes which can then
significantly benefit other algorithms that explicitly align the models. We demonstrate the utility of our
method on a wide variety of geometry processing applications.
AUTHOR = "N. J. Mitra and L. Guibas and J. Giesen and M. Pauly",
TITLE = "Probabilistic Fingerprints for Shapes",
BOOKTITLE = "Symposium on Geometry Processing",
YEAR = "2006",