We present an object recognition algorithm that uses model and image line features to locate complex objects in high clutter environments. Corresponding line features are determined by a three-stage process. The first stage generates a large number of approximate pose hypotheses from correspondence of one or two lines in the model and image. Next, pose hypotheses from the previous stage are quickly evaluated and ranked by a comparison of local image neighborhoods to the corresponding local model neighborhoods. Fast nearest neighbor and range search algorithms are used to implement a distance measure that is unaffected by clutter and partial occlusion. The ranking of pose hypotheses is invariant to changes in image scale, orientation, and partially invariant to affine distortion. Finally, a robust pose estimation algorithm is applied for refinement and verification, starting from the few best approximate poses produced by the previous stages. Experiments on real images demonstrate robost recognition of partially occluded objects in very high clutter environments.
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