A discriminative local shape descriptor plays an important role in various applications.In this paper,we present a novel deep learning framework that derives discriminative local descriptors for deformable 3D shapes.W...A discriminative local shape descriptor plays an important role in various applications.In this paper,we present a novel deep learning framework that derives discriminative local descriptors for deformable 3D shapes.We use local"geometry images"to encode the multi-scale local features of a point,via an intrinsic parameterization method based on geodesic polar coordinates.This new parameterization provides robust geometry images even for badly-shaped triangular meshes.Then a triplet network with shared architecture and parameters is used to perform deep metric learning;its aim is to distinguish between similar and dissimilar pairs of points.Additionally,a newly designed triplet loss function is minimized for improved,accurate training of the triplet network.To solve the dense correspondence problem,an efficient sampling approach is utilized to achieve a good compromise between training performance and descriptor quality.During testing,given a geometry image of a point of interest,our network outputs a discriminative local descriptor for it.Extensive testing of non-rigid dense shape matching on a variety of benchmarks demonstrates the superiority of the proposed descriptors over the state-of-the-art alternatives.展开更多
Vast amounts of data are produced with the development of smart cities and urban computing technologies.The data is often captured from multiple sensors,with heterogeneous structures and highly decentralized connectio...Vast amounts of data are produced with the development of smart cities and urban computing technologies.The data is often captured from multiple sensors,with heterogeneous structures and highly decentralized connections.Integrated data representation and smart computational models are required for more complex tasks in urban computing.We dwell deeply on two fundamental questions—can we provide an integrated data representation for the whole cyber–physical–social system?And,can we provide an integrated framework to choose the appropriate data for understanding a specific urban event?A holography data representation and the quasi-holography computational model have been proposed to address these problems.We describe case studies using the quasi-holography computational model,and discuss further problems to solve regarding our model.展开更多
基金partially funded by the National Key R&D Program of China(2018YFB2100602)the National Natural Science Foundation of China(61802406,61772523,61702488)+2 种基金Beijing Natural Science Foundation(L182059)the CCF–Tencent Open Research Fund,Shenzhen Basic Research Program(JCYJ20180507182222355)the Open Project Program of the State Key Lab of CAD&CG(A2004)Zhejiang University.
文摘A discriminative local shape descriptor plays an important role in various applications.In this paper,we present a novel deep learning framework that derives discriminative local descriptors for deformable 3D shapes.We use local"geometry images"to encode the multi-scale local features of a point,via an intrinsic parameterization method based on geodesic polar coordinates.This new parameterization provides robust geometry images even for badly-shaped triangular meshes.Then a triplet network with shared architecture and parameters is used to perform deep metric learning;its aim is to distinguish between similar and dissimilar pairs of points.Additionally,a newly designed triplet loss function is minimized for improved,accurate training of the triplet network.To solve the dense correspondence problem,an efficient sampling approach is utilized to achieve a good compromise between training performance and descriptor quality.During testing,given a geometry image of a point of interest,our network outputs a discriminative local descriptor for it.Extensive testing of non-rigid dense shape matching on a variety of benchmarks demonstrates the superiority of the proposed descriptors over the state-of-the-art alternatives.
基金This work was supported by National Basic Research Program of China(973 Program)(2015CB352501,2015CB352500).
文摘Vast amounts of data are produced with the development of smart cities and urban computing technologies.The data is often captured from multiple sensors,with heterogeneous structures and highly decentralized connections.Integrated data representation and smart computational models are required for more complex tasks in urban computing.We dwell deeply on two fundamental questions—can we provide an integrated data representation for the whole cyber–physical–social system?And,can we provide an integrated framework to choose the appropriate data for understanding a specific urban event?A holography data representation and the quasi-holography computational model have been proposed to address these problems.We describe case studies using the quasi-holography computational model,and discuss further problems to solve regarding our model.
