Analysis of urban spatial structures is an effective way to explain and solve increasingly serious urban problems.However,many of the existing methods are limited because of data quality and availability,and usually y...Analysis of urban spatial structures is an effective way to explain and solve increasingly serious urban problems.However,many of the existing methods are limited because of data quality and availability,and usually yield inaccurate results due to the unclear description of urban social functions.In this paper,we present an investigation on urban social function based spatial structure analysis using building footprint data.An improved turning function(TF)algorithm and a selforganizing clustering method are presented to generate the variable area units(VAUs)of high-homogeneity from building footprints as the basic research units.Based on the generated VAUs,five spatial metrics are then developed for measuring the morphological characteristics and the spatial distribution patterns of buildings in an urban block.Within these spatial metrics,three models are formulated for calculating the social function likelihoods of each urban block to describe mixed social functions in an urban block,quantitatively.Consequently,the urban structures can be clearly observed by an analysis of the spatial distribution patterns,the development trends,and the hierarchy of different social functions.The results of a case study conducted for Munich validate the effectiveness of the proposed method.展开更多
Realistic texture mapping and coherent up-to-date rendering is one of the most important issues in indoor 3-D modelling.However,existing texturing approaches are usually performed manually during the modelling process...Realistic texture mapping and coherent up-to-date rendering is one of the most important issues in indoor 3-D modelling.However,existing texturing approaches are usually performed manually during the modelling process,and cannot accommodate changes in indoor environments occurring after the model was created,resulting in outdated and misleading texture rendering.In this study,a structured learning-based texture mapping method is proposed for automatic mapping a single still photo from a mobile phone onto an alreadyconstructed indoor 3-D model.The up-to-date texture is captured using a smart phone,and the indoor structural layout is extracted by incorporating per-pixel segmentation in the FCN algorithm and the line constraints into a structured learning algorithm.This enables real-time texture mapping according to parts of the model,based on the structural layout.Furthermore,the rough camera pose is estimated by pedestrian dead reckoning(PDR)and map information to determine where to map the texture.The experimental results presented in this paper demonstrate that our approach can achieve accurate fusion of 3-D triangular meshes with 2-D single images,achieving low-cost and automatic indoor texture updating.Based on this fusion approach,users can have a better experience in virtual indoor3-D applications.展开更多
基金funded by the National Key Research and Development Program of China(No.2018YFB0505400)the National Natural Science Foundation of China Project(Grant Nos.42071370,41771484).
文摘Analysis of urban spatial structures is an effective way to explain and solve increasingly serious urban problems.However,many of the existing methods are limited because of data quality and availability,and usually yield inaccurate results due to the unclear description of urban social functions.In this paper,we present an investigation on urban social function based spatial structure analysis using building footprint data.An improved turning function(TF)algorithm and a selforganizing clustering method are presented to generate the variable area units(VAUs)of high-homogeneity from building footprints as the basic research units.Based on the generated VAUs,five spatial metrics are then developed for measuring the morphological characteristics and the spatial distribution patterns of buildings in an urban block.Within these spatial metrics,three models are formulated for calculating the social function likelihoods of each urban block to describe mixed social functions in an urban block,quantitatively.Consequently,the urban structures can be clearly observed by an analysis of the spatial distribution patterns,the development trends,and the hierarchy of different social functions.The results of a case study conducted for Munich validate the effectiveness of the proposed method.
基金supported by the National Key Research and Development Program of China[grant number 2016YFB0502203]the National Natural Science Foundation of China Project[41701445]The State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing of Wuhan University.
文摘Realistic texture mapping and coherent up-to-date rendering is one of the most important issues in indoor 3-D modelling.However,existing texturing approaches are usually performed manually during the modelling process,and cannot accommodate changes in indoor environments occurring after the model was created,resulting in outdated and misleading texture rendering.In this study,a structured learning-based texture mapping method is proposed for automatic mapping a single still photo from a mobile phone onto an alreadyconstructed indoor 3-D model.The up-to-date texture is captured using a smart phone,and the indoor structural layout is extracted by incorporating per-pixel segmentation in the FCN algorithm and the line constraints into a structured learning algorithm.This enables real-time texture mapping according to parts of the model,based on the structural layout.Furthermore,the rough camera pose is estimated by pedestrian dead reckoning(PDR)and map information to determine where to map the texture.The experimental results presented in this paper demonstrate that our approach can achieve accurate fusion of 3-D triangular meshes with 2-D single images,achieving low-cost and automatic indoor texture updating.Based on this fusion approach,users can have a better experience in virtual indoor3-D applications.
