We present a novel method for 3D-object retrieval using Bag-of-Feature (BoF) approaches. The method starts by selecting and then describing a set of points from the 3D-object. The proposed descriptor is an indexed collection of closed curves in R3 on the 3D-surface. Such descriptor has the advantage of being invariant to different transformations that a shape can undergo. Based on vector quantization, we cluster those descriptors to form a shape vocabulary. Then, each point selected in the object is associated to a cluster (word) in that vocabulary. Finally, a BoF histogram counting the occurrences of every word is computed. In order to assess our method, we used shapes from the TOSCA and Sumner datasets. The results clearly demonstrate that the method is robust to many kind of transformations and pro- duces higher precision compared with some state-of-the-art methods.