Abstract: In order to make use of 3D model data set for feature self-learning more effectrively,this paper proposed a 3D model retrieval method,in which the natural images are as input sources,a better view set of 3D model is as basis,and the depth features obtained through training deep convolutional neural networks are applied for retrieval.Firstly,the 3D model views are extracted from multiple viewpoints and the optimal view is selected according to the order of gray entropy.Secondly,the view set is trained through deep convolution neural network,the depth features of the optimal view are extracted and its dimension is reduced.At the same time,edge contouring is extracted for the input natural image and a set of 3D models is gotten after similarity matching.Finally,according to the ratio of the total number of similar models to the length of the search list in the retrieval result,the retrieval list is reordered and the final result will be gained.Experimental results show that the algorithm can effectively use depth convolution neural network to extract the depth feature of the view of the 3D model,meanwhile reduce the difficulty of obtaining the input source,and improve the retrieval efficiency effectively.

Key words: 3D model retrieval, Deep learning, Feature extraction, View selection