基于Leap Motion的三维模型草图检索
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摘要
对三维模型检索的需求在搜索领域日益增长,而基于草图的三维模型检索也已然成为最为有效且易用的检索方式。为了更好地提升草图检索的体验,着眼于人机交互的方式并对其加以改进是必由之路。其中,Leap Motion系统可高精度实时检测并跟踪手指的位置和手势信息等,为大屏幕、远距离交互提供了有效途径。文中提出了一种基于Leap Motion的三维模型草图检索系统用于三维模型检索。通过Leap Motion获得的数据将手的运动模拟信息实时地反映到浏览器上,实现草图的绘制和模型的检索。在草图检索部分,提出了一种改进的流形排序方法用于对模型间的相似度进行排序,结合模型语义分类信息的排序约束方法,提高了同类模型的排名分数。实验显示,该方法使得草图检索更加贴近和方便用户,进一步提高了草图检索的易用性以及三维模型检索的准确性。
The demand of 3D model retrieval is increasing,and the sketch-based method has been proved to be the most effective and efficient approach to retrieve 3D models. In order to get better experience on sketch retrieval,it's necessary to focus on the way of human-computer interaction as well as improve it. The Leap Motion sensor can detect the position information of fingers and gestures with high precision in real-time,which makes volume interaction with large screen at long-distance more effective. In this paper,an extended manifold ranking method based on Leap Motion is presented as a new retrieval framework. The sketch drawing and model retrieval processes are realized by obtaining data of hands from Leap Motion to the browser. In the process of sketch retrieval,manifold ranking is proposed to rank on similarities between models combining with semantic classification as a constraint,thus improving the ranking score of models belong to the same classification. Experiments have demonstrated that the proposed method makes the sketch retrieval more closable and convenient to the user as well as improves the accuracy of 3D model retrieval.
The demand of 3D model retrieval is increasing,and the sketch-based method has been proved to be the most effective and efficient approach to retrieve 3D models. In order to get better experience on sketch retrieval,it's necessary to focus on the way of human-computer interaction as well as improve it. The Leap Motion sensor can detect the position information of fingers and gestures with high precision in real-time,which makes volume interaction with large screen at long-distance more effective. In this paper,an extended manifold ranking method based on Leap Motion is presented as a new retrieval framework. The sketch drawing and model retrieval processes are realized by obtaining data of hands from Leap Motion to the browser. In the process of sketch retrieval,manifold ranking is proposed to rank on similarities between models combining with semantic classification as a constraint,thus improving the ranking score of models belong to the same classification. Experiments have demonstrated that the proposed method makes the sketch retrieval more closable and convenient to the user as well as improves the accuracy of 3D model retrieval.
引文
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[17]SHILANE P,MIN P,KAZHDAN M,et al.The Princeton shape benchmark[C]//In Proceedings of the International Conference on Shape Modeling and Applications.New York:IEEE Press,2004:167-178.
[2]LI B,LU Y J,GODIL A,et al.SHREC’13 track:large scale sketch-based 3D shape retrieval[C]//Proceedings of the Sixth Eurographics Workshop on 3D Object Retrieval.Switzerland:Eurographics Association Published,2013:89-96.
[3]LI B,LU Y,LI C,et al.SHREC’14 track:extended large scale sketch-based 3d shape retrieval[C]//Eurographics Workshop on 3D Object Retrieval,EG 3DOR.Switzerland:Eurographics Association Published,2014:121-130.
[4]ZOU C,WANG C,WEN Y,et al.Viewpoint-aware representation for sketch-based 3D model retrieval[J].IEEE Signal Processing Letters,2014,21(8):966-970.
[5]LI B,LU Y,LI C,et al.A comparison of 3D shape retrieval methods based on a large-scale benchmark supporting multimodal queries[J].Computer Vision&Image Understanding 2015,131:1-27.
[6]FURUYA T,OHBUCHI R.Similarity metric learning for sketch-based 3D object retrieval[J].Multimedia Tools and Applications,2015,74(23):10367-10392.
[7]卢浩然,张宇,牛苗苗,等.基于Leap motion的三维文物虚拟拼接方法[J].系统仿真学报,2015,27(12):3006-3011,3017.
[8]徐崇斌,周明全,沈俊辰,等.一种基于Leap Motion的直观体交互技术[J].电子与信息学报,2015,37(2):353-359.
[9]潘佳佳,徐昆.基于Leap Motion的三维自由手势操作[J].中国科技论文,2015,10(2):207-212.
[10]LI B,LU Y J,GHUMMAN A,et al.3D sketch-based 3D model retrieval[C]//Proceedings of the 5th ACM on International Conference on Multimedia Retrieval.New York:CAM Press,2015:555-558.
[11]SIVIC J,ZISSERMAN A.Video Google:a text retrieval approach to object matching in videos[C]//IEEE International Conference on Computer Vision.New York:IEEE Press,2003:1470-1477.
[12]EITZ M,HAYS J,ALEXA M.How do humans sketch objects?[J].ACM Transactions on Graphics,2012,31(4):44.
[13]JUDD T,DURAND F,ADELSON E.Apparent ridges for line drawing[J].ACM Transactions on Graphics,2007,26(3):19.
[14]BOSCH A,ZISSERMAN A,MUNOZ X.Representing shape with a spatial pyramid kernel[C]//In Proceedings of the 6th ACM International Conference on Image and Video Retrieval.New York:ACM Press,2007:401-408.
[15]ZOBEL J.MOFFAT A.Inverted files for text search engines[J].ACM Computing Surveys,2006,38(2):6
[16]ZHOU D,BOUSQUET O,LAL T N,et al.Learning with local and global consistency[J].Advances in Neural Information Processing Systems,2004,16(4):321-328.
[17]SHILANE P,MIN P,KAZHDAN M,et al.The Princeton shape benchmark[C]//In Proceedings of the International Conference on Shape Modeling and Applications.New York:IEEE Press,2004:167-178.