Spin Image Revisited: Fast Candidate Selection Using Outlier Forest Search

Abstract

Spin-images have been widely used for surface registration and object detection from range images in that they are scale, rotation, and pose invariant. The computational complexity, however, is linear to the number of spin images in the model data set because valid candidates are chosen according to the similarity distribution between the input spin image and whole spin images in the data set. In this paper we present a fast method for valid candidate selection as well as approximate estimate of the similarity distribution using outlier search in the partitioned vocabulary trees. The sampled spin images in each tree are used for approximate density estimation and best matched candidates are then collected in the trees according to the statistics of the density. In contrast to the previous approaches that attempt to build compact representations of the spin images, the proposed method reduces the search space using the hierarchical clusters of the spin images such that the computational complexity is drastically reduced from O(K·N) to O(K·logN). K and N are the size of the spin-image features and the model data sets respectively. As demonstrated in the experimental results with a consumer depth camera, the proposed method is tens of times faster than the conventional method while the registration accuracy is preserved.