A new method, called feature extraction by demands (FED), for generating an object description concurrently at different feature levels will be described. An object is described in terms of features which include points, surface patches, edges, corners, and surfaces. These features form a feature space which is the base used to decompose the feature extraction process into different levels. FED provides a method to generate partial descriptions about objects from partially processed range data at different feature levels. The partial descriptions become a feed-back to guide the feature extraction process to extract more detailed information from interesting areas which can then be used to refine the object description. Regions which are not perceived to contain useful infomation will be ignored in further processing. As a more complete object description is generated, FED converges from bottom-up image processing to top-down hypotheses verification to generate complete hierarchical object descriptions.