Using Text Representation of Point Clouds to Solve the Recognition Problem

In this paper, we propose a compact textual representation of scanned 3D objects. This representation can be obtained from a source point cloud by extracting geometric primitives, forming a topological graph displaying the relative positions of the primitives in the object, and then converting the graph into a character string. Our experiments show that many objects can be described quite accurately as a set of geometric primitives. Furthermore, the proposed representation significantly reduces the memory footprint compared to the source point cloud. The object recognition problem can be solved by searching the database for an object whose textual representation is closest in terms of the Levenshtein distance to the textual representation of the object being recognized. Computational experiments show that the proposed representation enables achieving a fairly high recognition accuracy.