Model group indexing for recognition

To avoid the excessive computation of testing all combinations of feature matches groups of model features can be arranged in an index space offline (hashed). Ideally each image group should index into the space and find only those model groups that could have formed that image group. We prove an unexpected tight lower bound on the space required for such an indexing scheme for point features 3D models and 2D images and consider some implementation issues.

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