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Recent developments in 3D sensors have raised the possibility of using them in an increasing number of engineering applications. However, since most 3D sensors, such as the laser range finder, are based on the use of light, which moves in straight lines, the measurement area is limited to the front of an object, making the back an ""invisible"" surface. To calculate such unmeasurable areas, a system that memorizes shapes often encountered in objects and superimposes them on the scene is required. To realize such a type of system, an appropriate 3D shape representation is needed. This representation should 1) be able to handle and compare partial and complete sets of data of object shapes, and 2) operate quickly enough to be applicable to real-time tasks.  We developed a novel shape representation framework called ""Internal Radiated-light Projection (IRP)"" to represent and compare 3D objects. This representation projects local shape information of an object on a sphere by imaginary rays from the ""kernel"" of the object. To describe local shape information and arrange shapes properly, we propose Harmonic Contour Analysis (HCA) and the Shape Matrix.  These concepts are characterized by 1) simplicity; 2) the use of local shapes and their adjacent information; and, by using the Shape Matrix, 3) the consideration of the effect of gravity and stable poses for objects. In IRP representation, we can categorize objects in known classes and calculate their positions and attitudes. This paper explains the basic concept behind IRP, which is a way of representing local 3D shapes by HCA and categorizing them using the Shape Matrix. We then present experiments in object recognition for both virtual and real objects to demonstrate its efficiency and feasibility.

Study of Shape Representation Using Internal Radiated-light Projection