City Routing Velocity Estimation Model under theEnvironment of Lack of Floating Car Data

After introducing the principle of float car data (FCD), this paper gives the primary flow of pre-handing and map- matching of the FCD. After analyzing the percentage of coverage of FCD on the road network, large quantity of heritage database of routing status is used to estimate the routing velocity when lack of FCD on parts road segments. Multi liner regression model is then put forwarded by considering the spatial correlativity among the road network, and some model parameters are deduced when time series is classified in day and week. Besides, error of velocity probability and error of status probability are achieved based on the result from field testing while the feasibility and reliability of the velocity estimation model is obtained as well. Finally, as a case study in Shanghai center area, the whole routing velocity in the road network is estimated and published in real time.

Automatic extraction and reconstruction of a 3D wireframe of an indoor scene from semantic point clouds

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.

Detecting repetitive structures on building footprints for the purposes of 3D modeling and reconstruction

Repetitive structures of a building share features in terms of geometries and appearance and,therefore,the 3D information for these structures can be transferred from one specification to another for the purpose of 3D modeling and reconstruction once they are identified as repetitive structures.In this paper,a novel approach is proposed for the detection of the repetitive structures specified by the polygons of a building’s footprints.Instead of directly operating on the polygon in 2D space,the polygon is converted into a bend angle function representation in 1D space,whereby an extrusion is represented as a closed polygon intersected by the x-axis and located above it,while an intrusion is represented as a closed polygon below the x-axis.In this way,a polygon of a footprint is decomposed into a number of extrusions and intrusions which can in turn be processed.The task of detecting any repetitive structures specified in a building’s footprints then becomes the task of clustering the intersected polygons in the bend angle function space.The extrusions/intrusions which can be placed in the same clusters can be regarded as repetitive structures.Experiments show that this proposed approach can detect repetitive structures with different sizes,orientations and complexities.