The sample tree: a sequential hypothesis testing approach to 3D object recognition

Abstract

A method is presented for efficient and reliable object recognition within noisy, cluttered, and occluded range images. The method is based on a strategy which hypothesizes the intersection of the object with some selected image point, and searches for additional surface data at locations relative to that point. At each increment, the image is queried for the existence of surface data at a specific spatial location, and the set of possible object poses is further restricted. Eventually, either the object is identified and localized, or the initial hypothesis is refuted. The strategy is implemented in the discrete domain as a binary decision tree classifier. The tree leaf nodes represent individual voxel templates of the model. The internal tree nodes represent the union of the templates of their descendant leaf nodes. The union of all leaf node templates is the complete template set of the model over its discrete pose space. Each internal node also references a single voxel which is the most common element of its child node templates. Traversing the tree is equivalent to efficiently matching the large set of templates at a selected image seed location. The process is approximately 3 orders of magnitude more efficient than brute-force template matching. Experimental results are presented in which objects are reliably recognized and localized in 6 dimensions in less than 60 seconds within noisy and significantly occluded range images.