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In this paper, a novel local surface descriptor is proposed and applied to the problem of aligning partial views of a 3D object. The descriptor is based on taking "snapshots” of the surface over each point using a virtual camera oriented perpendicularly to the surface. This representation has the advantage of imposing minimal loss of information be robust to self-occlusions and also be very efficient to compute. Then, we describe an efficient search technique to deal with the rotation ambiguity of our representation and experimentally demonstrate the benefits of our approaches which are pronounced especially when we align views with small overlap.
Surface matching, object recognition, partially overlapping surfaces.

S. Malassiotis and M. G. Strintzis, "Snapshots: A Novel Local Surface Descriptor and Matching Algorithm for Robust 3D Surface Alignment," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 29, no. , pp. 1285-1290, 2007.
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