Issues in geometric reasoning from range imagery

The authors discuss some of the issues that have to be tackled in order to perform geometric reasoning from range imagery. They begin by pointing out that a successful system must deal with real-world data, and therefore take into account the effects of noise and quantization. They suggest that adaptive smoothing may prove to be a helpful tool for such a task. The next stage of processing involves a symbolic representation of the original data. The authors spell out criteria for shape description, discuss current representation schemes and point out their limitations, and then propose some ideas for overcoming such limitations, illustrated on real examples. Finally, they look at the issues in recognition, and more specifically the matching part, with reference to different methodologies, tree search and constraint satisfaction network