<title>Model-based recognition of three-dimensional objects from incomplete range data</title>

Abstract

The recognition of 3D objects from images is an important problem in computer vision. Recently, the use of range images has gained increasing popularity because of the explicit representation of 3D shape information in range data. In our work we are using range images acquired by a sensor based on the coded light principle. This type of range scanner has recently become commercially available at low cost. But it suffers from the fact that no range data can be acquired in shadow areas of the input scene. Thus, the range data provided by the scanner is incomplete, in general. This fact leads to significant complications in object recognition. In the present paper we describe a model-based recognition system that is robust if there are regions of missing data in an input range image. The system uses a graph matching technique that searches for an optimal correspondence between surface patches extracted from an input range image and objects from a model library. In the model matching process, a hierarchy of constraints aiming at the recognition of objects with few distortions first. As long as there are unmatched surface patches in the input data, we gradually relax the constraints. The system works for both objects with planar and curved surfaces. In practical experiments, it has shown a high degree of robustness on images of complex scenes in which significant portions of the data were missing. A correct recognition rate of 92% with 8% of the objects being rejected has been achieved on a test set.