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We present a method for the recovery of partially occluded 3D geometric primitives from range images which might also include nonprimitive objects. The method uses a technique for estimating the principal curvatures and Darboux frame from range images. After estimating the principal curvatures and the Darboux frames from the entire scene, a search for the known patterns of these features in geometric primitives is performed. If a specific pattern is identified, then the presence of the corresponding primitive is confirmed by using these local features. The features are also used to recover the primitive's characteristics. The suggested application is very efficient since it combines the segmentation, classification, and fitting processes, which are part of any recovery process, in a single process, which advances monotonously through the recovery procedure. We view the problem as a robust statistics problem, and we therefore use techniques from that field. A mean-shift-based algorithm is used for the robust estimation of shape parameters, such as recognizing which types of shapes in the scene exist and, after that, full recovery of planes, spheres, and cylinders. A random-sample-consensus-based algorithm is used for robust model estimation for the more complex primitives, such as cones and tori. As a result of these algorithms, a set of proposed primitives is found. This set contains superfluous models which cannot be detected at this stage. To deal with this problem, a minimum-description-length method has been developed, which selects a subset of models that best describes the scene. The method has been tested on series of real complex cluttered scenes, yielding accurate and robust recoveries of primitives.