Using Dihedral Angles for Edge Extraction in Range Data

The volume of raw range image data that is required to represent just a single scene can be extensive; hence direct interpretation of range images can incur a very high computational cost. Range image feature extraction has been identified as a mechanism to produce a more compact scene representation, in particular using features such as edges and surfaces, and hence enables less costly scene interpretation for applications such as object recognition and robot navigation. We present an approach to edge detection in range images that can be used directly with any range data, regardless of whether the data have regular or irregular spatial distribution. The approach is evaluated with respect to accuracy of both edge location and visual results are also provided.