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Three-dimensional object recognition using average surface normal detection

A direction characterization of a surface region in a range image, called the average surface normal (ASN), is presented in this paper. The goal is to robustly detect a direction for each range image pixel that is object centered and reasonably insensitive to the viewpoint. The average surface normal for a range image pixel is defined as the normal to that best-fitting plane of a local region of the image that surrounds the pixel. An analysis of the noise sensitivity of the ASN is presented and the bias due to Gaussian noise on a plane surface is determined. Results from empirical experiments are presented that confirm that the ASN operator has a very small bias in the presence of noise. The use of the ASN to aid three- dimensional object identification is considered. A three-dimensional object may be decomposed into a number of similar sized detectable surface regions each of which defines an object feature. Object identification is achieved by detecting a visible subset of these features.