3D object retrieval involves more efforts mainly because major computer vision features are designed for 2D images, which is rarely applicable for 3D models. In this paper, we propose to retrieve the 3D models based on the implicit parameters learned from the radial base functions that represent the 3D objects. The radial base functions are learned from the RBF neural network. As deep neural networks can represent the data that is not linearly separable, we apply multiple layers' neural network to train the radial base functions. Our feature can be applied to recover the 3D objects, which proves the effectiveness of our features in representing the 3D objects. Furthermore, the dimensionality of the learned feature is scalable, which leads to memory efficiency. Experiments demonstrate the accuracy of our feature in 3D model retrieval.