Johnson and Hebert?s spin-images have been applied to the registration of range images and object recognition with much success because they are rotation, scale, and pose invariant. In this paper we address two issues concerning spin-images, namely: (1) comparing uncompressed spinimages across large datasets is costly, and (2) a method to select the appropriate bin size and image width for spinimages is not clearly defined.

Our solution to these issues is a multi-resolution method that generates a pyramid of spin-images by successively decreasing the spin-image size by powers of two. To efficiently correlate surface points, we compare spin-images in a low-to-high resolution manner. Once multi-resolution spin-images are generated for a given object, we have found that the different resolutions can also be used to compare objects that have differing or non-uniform point densities. To select the appropriate bin sizes for comparing such objects, we use the ratio of the average edge lengths of the objects. We also show preliminary results of using the pyramid to converge on the appropriate image width by traversing the pyramid in a low-to-high resolution manner looking for the highest resolution at which the fewest number of highly correlated points are found to match a given feature point.