Semantic query optimization (SQO) is comparatively a recent approach for the transformation of given query into equivalent alternative query using matching rules in order to select an optimal query based on the costs ...Semantic query optimization (SQO) is comparatively a recent approach for the transformation of given query into equivalent alternative query using matching rules in order to select an optimal query based on the costs of executing alternative queries. The key aspect of the algorithm proposed here is that previous proposed SQO techniques can be considered equally in the uniform cost model, with which optimization opportunities will not be missed. At the same time, the authors used the implication closure to guarantee that any matched rule will not be lost. The authors implemented their algorithm for the optimization of decomposed sub-query in local database in Multi-Database Integrator (MDBI), which is a multidatabase project. The experimental results verify that this algorithm is effective in the process of SQO.展开更多
Accurate indoor 3D models are essential for building administration and applications in digital city construction and operation.Developing an automatic and accurate method to reconstruct an indoor model with semantics...Accurate indoor 3D models are essential for building administration and applications in digital city construction and operation.Developing an automatic and accurate method to reconstruct an indoor model with semantics is a challenge in complex indoor environments.Our method focuses on the permanent structure based on a weak Manhattan world assumption,and we propose a pipeline to reconstruct indoor models.First,the proposed method extracts boundary primitives from semantic point clouds,such as floors,walls,ceilings,windows,and doors.The primitives of the building boundary,are aligned to generate the boundaries of the indoor scene,which contains the structure of the horizontal plane and height change in the vertical direction.Then,an optimization algorithm is applied to optimize the geometric relationships among all features based on their categories after the classification process.The heights of feature points are captured and optimized according to their neighborhoods.Finally,a 3D wireframe model of the indoor scene is reconstructed based on the 3D feature information.Experiments on three different datasets demonstrate that the proposed method can be used to effectively reconstruct 3D wireframe models of indoor scenes with high accuracy.展开更多
文摘Semantic query optimization (SQO) is comparatively a recent approach for the transformation of given query into equivalent alternative query using matching rules in order to select an optimal query based on the costs of executing alternative queries. The key aspect of the algorithm proposed here is that previous proposed SQO techniques can be considered equally in the uniform cost model, with which optimization opportunities will not be missed. At the same time, the authors used the implication closure to guarantee that any matched rule will not be lost. The authors implemented their algorithm for the optimization of decomposed sub-query in local database in Multi-Database Integrator (MDBI), which is a multidatabase project. The experimental results verify that this algorithm is effective in the process of SQO.
基金supported by the National Key Research and Development Program of China(Grant No.2021YFB2501103)the National Science Foundation of China(Grant No.42271429 and 42130106)the Key Research and Development Projects of Shanghai Science and Technology Commission(Grant No.21DZ1204103).
文摘Accurate indoor 3D models are essential for building administration and applications in digital city construction and operation.Developing an automatic and accurate method to reconstruct an indoor model with semantics is a challenge in complex indoor environments.Our method focuses on the permanent structure based on a weak Manhattan world assumption,and we propose a pipeline to reconstruct indoor models.First,the proposed method extracts boundary primitives from semantic point clouds,such as floors,walls,ceilings,windows,and doors.The primitives of the building boundary,are aligned to generate the boundaries of the indoor scene,which contains the structure of the horizontal plane and height change in the vertical direction.Then,an optimization algorithm is applied to optimize the geometric relationships among all features based on their categories after the classification process.The heights of feature points are captured and optimized according to their neighborhoods.Finally,a 3D wireframe model of the indoor scene is reconstructed based on the 3D feature information.Experiments on three different datasets demonstrate that the proposed method can be used to effectively reconstruct 3D wireframe models of indoor scenes with high accuracy.
