Patent ID: 8345988

Claim:
A method for recognizing an object in a scene coordinate system with a set of model features from a plurality of sets of model features, comprising: obtaining a set of scene features associated with the object from the scene coordinate system; pruning the set of model features from a database, based on a comparison between the set of scene features and the set of model features to produce a pruned set of model features by applying a set of normals representing surface descriptor parameters to prune bogus model features, wherein the surface descriptor parameters comprise a location and a spin image; generating a set of pose hypotheses based on a pair of scene features selected from the set of scene features; removing one or more bogus pose hypotheses from the set of pose hypotheses through the use of geometric constraints, thereby creating a refined set of pose hypotheses; pruning said refined set of pose hypotheses based on a comparison between each pose hypotheses from the refined set of pose hypotheses and a pair of model features selected from the set of pruned model features by: computing the probability of collision between generated hash values of each of the refined set of pose hypotheses and the pair of model features and retaining those pose hypotheses from the refined set of pose hypotheses and the pair of model features whose probability of collision is above a threshold probability; and selecting a verified pose hypothesis from the pruned and refined set of pose hypotheses by: transforming the pruned set of model features into the scene coordinate system, calculating a similarity score for each pose hypothesis from the pruned and refined set of pose hypotheses based on a similarity between the each pose hypothesis and the transformed set of model features, and identifying the verified pose hypothesis associated with a highest similarity score.