Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In recent years, with the development of 3D technologies, 3D model retrieval has become a hot topic. The key point of 3D model retrieval is to extract robust feature for 3D model representation. In order to improve the effectiveness of method on 3D model retrieval, this paper proposes a feature extraction model based on convolutional neural networks (CNN). First,  extract a set of 2D images from 3D model to represent each 3D object. SIFT detector is utilized to detect interesting points from each 2D image and extract interesting patches to represent local information of each 3D model. X-means is leveraged to generate the CNN filters. Second, a single CNN layer learns low-level features which are then given as inputs to multiple recursive neural networks (RNN) in order to compose higher order features. RNNs can generate the final feature for 2D image representation. Finally, nearest neighbor is used to compute the similarity beten different 3D models in order to handle the retrieval problem. Extensive comparison experiments re on the popular ETH and MV-RED 3D model datasets. The results demonstrate the superiority of the proposed method.
['Weizhi Nie', 'Qun Cao', 'Anan Liu', 'Yuting Su']
I am writing a research paper on a idea Convolutional deep learning for 3D object retrieval
[{"from": "human", "value": "I am writing a research paper on a idea Convolutional deep learning for 3D object retrieval"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In recent years, with the development of 3D technologies, 3D model retrieval has become a hot topic. The key point of 3D model retrieval is to extract robust feature for 3D model representation. In order to improve the effectiveness of method on 3D model retrieval, this paper proposes a feature extraction model based on convolutional neural networks (CNN). First,  extract a set of 2D images from 3D model to represent each 3D object. SIFT detector is utilized to detect interesting points from each 2D image and extract interesting patches to represent local information of each 3D model. X-means is leveraged to generate the CNN filters. Second, a single CNN layer learns low-level features which are then given as inputs to multiple recursive neural networks (RNN) in order to compose higher order features. RNNs can generate the final feature for 2D image representation. Finally, nearest neighbor is used to compute the similarity beten different 3D models in order to handle the retrieval problem. Extensive comparison experiments re on the popular ETH and MV-RED 3D model datasets. The results demonstrate the superiority of the proposed method."}]