Large databases of 3D models available in public domain have created the demand for shape search and retrieval algorithms capable of finding similar shapes in the same way a search engine responds to text quires. Since many shapes manifest rich variability, shape retrieval is often required to be invariant to different classes of transformations and shape variations. One of the most challenging settings in the case of non-rigid shapes, in which the class of transformations may be very wide due to the capability of such shapes to bend and assume different forms.
> In this talk, we will apply modern methods in computer vision to problems of non-rigid shape analysis. Feature-based methods such as the Scale-Invariant Feature Transform (SIFT) have recently gained popularity in computer vision, while remaining largely unknown in the shape analysis community. We will show analogous approaches in the 3D world applied to the problem of non-rigid shape retrieval in large (Internet-scale) databases. This will allow us to adopt methods employed in search engines for efficient indexing and search of shapes. To conclude, we will show “Shape Google'', a prototype search engine for deformable shapes.