In this paper we propose a two-step approach to recognize free-form objects in range images. First

feature points are calculated based on the contour curve

then mapped to the original shape space. Then a landmark list is determined and used to form a rejection classifier

which quickly eliminates a large number of candidates for an efficient recognition. The remaining free-form objects are then verified using a novel local patch-based matching approach

which is robust to occlusions and noise. The key points are determined based on the scale invariant feature transform (SIFT)

and the local surface descriptor is characterized by its two 1D histograms. The two histograms show the frequency of occurrence of the shape index values and the angles between the normal of a key point and that of its neighbors. In order to speed up the retrieval of surface descriptors and save the restore space

the modal data includes shape and surface information. The local surface patched of modal are indexed into a hash table. Verification is performed by running the Iterative Closest Point algorithm. Experimental results with ideal range image are presented to demonstrate the effectiveness

efficiency of the proposed approach.The approach is robust to occlusions and noise.