Structural hashing: efficient three dimensional object recognition

An approach for the recognition of multiple three-dimensional object models from three-dimensional scene data is presented. The authors work on dense data, but neither the models nor the scene data have to be complete. The problem is addressed in a realistic environment: the viewpoint is arbitrary, the objects vary widely in complexity, and no assumptions about the structure of the surface are made. The approach is novel in that it uses two different types of primitives for matching: small surface patches, where differential properties can be reliably computed, and lines corresponding to depth or orientation discontinuities. These are represented by splashes and 3-D curves respectively. It is shown how both of these primitives can be encoded by a set of super segments, consisting of connected linear segments. These super segments are entered into a hash table, and provide the essential mechanism for fast retrieval and matching