This paper proposes a technique for object recognition using superquadric built models. Superquadrics, which are three-dimensional models suitable for part-level representation of objects, are reconstructed from range images using the recover-and-select paradigm. Using interpretation trees, the presence of an object from the model database can be hypothesized. These hypotheses are verified by projecting and re-fitting the object model to the range image of the scene which at the same time enables a better localization of the object in the scene
