A model-based approach is described for recognizing and locating polyhedral objects from sparse light-stripe data. Based on two scans, consistent interpretations are provided by locating scanned line segments on the faces of a set of known objects. The transformation associated with each interpretation is estimated. Once all the feasible models are transformed to the scene space, a strategy for additional scanning positions is developed to distinguish the object uniquely.