The rapid development of computer vision has led to an increasing amount of 3 D data,such as multiple views and point clouds,which are widely used in 3 D object recognition and retrieval.Intuitively,the quality of 3 D...The rapid development of computer vision has led to an increasing amount of 3 D data,such as multiple views and point clouds,which are widely used in 3 D object recognition and retrieval.Intuitively,the quality of 3 D data is the most crucial factor that directly affects the performance of 3 D applications.However,how to evaluate the 3 D data quality,especially the multi-view data quality,is still an open question.To tackle this issue,we propose an entropy-based multi-view information quantification model(MV-Info model)to quantitatively evaluate the multi-view data information.Our proposed MV-Info model consists of hierarchical data module,feature generation module,and quantitative calculation module.Besides,it considers the information entropy theory for more reasonable quantification results.In our method,how much information we can observe from a group of views can be quantified,which can be used to support 3 D recognition and retrieval.We also designed a series of experiments to evaluate the effectiveness of the proposed model.The experimental results demonstrate the rationality and validity of the proposed model.展开更多
基金the SGCC Science and Technology Project(Grant No.52020119000A)。
文摘The rapid development of computer vision has led to an increasing amount of 3 D data,such as multiple views and point clouds,which are widely used in 3 D object recognition and retrieval.Intuitively,the quality of 3 D data is the most crucial factor that directly affects the performance of 3 D applications.However,how to evaluate the 3 D data quality,especially the multi-view data quality,is still an open question.To tackle this issue,we propose an entropy-based multi-view information quantification model(MV-Info model)to quantitatively evaluate the multi-view data information.Our proposed MV-Info model consists of hierarchical data module,feature generation module,and quantitative calculation module.Besides,it considers the information entropy theory for more reasonable quantification results.In our method,how much information we can observe from a group of views can be quantified,which can be used to support 3 D recognition and retrieval.We also designed a series of experiments to evaluate the effectiveness of the proposed model.The experimental results demonstrate the rationality and validity of the proposed model.
