Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: View-based 3D object retrieval techniques have become prevalent in various fields, and lots of ingenious studies have promoted the development of retrieval performance from different aspects. ,  focus on the 2D projective views that represent the 3D objects and propose a boosting approach by evaluating the discriminative ability of each object's views. Different from previous works on selecting representative views of query object,  investigate the discriminative information of each view in dataset. By employing the proposed reverse distance metric,  utilize the discriminative information for many to many view set matching. The proposed algorithm is then employed with various features to boost the multi-model graph learning method.  compare our approach with several state of the art methods on ETH-80 dataset and National Taiwan University 3D model dataset. The results demonstrate the effectiveness of our method and its excellent boosting performance.
['Dong Wang', 'Bin Wang', 'Sicheng Zhao', 'Hongxun Yao', 'Hong Liu']
I am writing a research paper on a idea Exploring Discriminative Views for 3D Object Retrieval
[{"from": "human", "value": "I am writing a research paper on a idea Exploring Discriminative Views 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: View-based 3D object retrieval techniques have become prevalent in various fields, and lots of ingenious studies have promoted the development of retrieval performance from different aspects. ,  focus on the 2D projective views that represent the 3D objects and propose a boosting approach by evaluating the discriminative ability of each object's views. Different from previous works on selecting representative views of query object,  investigate the discriminative information of each view in dataset. By employing the proposed reverse distance metric,  utilize the discriminative information for many to many view set matching. The proposed algorithm is then employed with various features to boost the multi-model graph learning method.  compare our approach with several state of the art methods on ETH-80 dataset and National Taiwan University 3D model dataset. The results demonstrate the effectiveness of our method and its excellent boosting performance."}]