scholarly journals Size-Adaptive Texture Atlas Generation and Remapping for 3D Urban Building Models

2021 ◽  
Vol 10 (12) ◽  
pp. 798
Author(s):  
Xuequan Zhang ◽  
Wei Liu ◽  
Bing Liu ◽  
Xin Zhao ◽  
Zihe Hu
Keyword(s):  
Large Scale ◽  
Texture Mapping ◽  
Graphics Hardware ◽  
High Fidelity ◽  
Blank Space ◽  
Web Based ◽  
Vertex Number ◽  
Building Model ◽  
Building Models ◽  
Atlas Method

A high-fidelity 3D urban building model requires large quantities of detailed textures, which can be non-tiled or tiled ones. The fast loading and rendering of these models remain challenges in web-based large-scale 3D city visualization. The traditional texture atlas methods compress all the textures of a model into one atlas, which needs extra blank space, and the size of the atlas is uncontrollable. This paper introduces a size-adaptive texture atlas method that can pack all the textures of a model without losing accuracy and increasing extra storage space. Our method includes two major steps: texture atlas generation and texture atlas remapping. First, all the textures of a model are classified into non-tiled and tiled ones. The maximum supported size of the texture is acquired from the graphics hardware card, and all the textures are packed into one or more atlases. Then, the texture atlases are remapped onto the geometric meshes. For the triangle with the original non-tiled texture, new texture coordinates in the texture atlases can be calculated directly. However, as for the triangle with the original tiled texture, it is clipped into many unit triangles to apply texture mapping. Although the method increases the mesh vertex number, the increased geometric vertices have much less impact on the rendering efficiency compared with the method of increasing the texture space. The experiment results show that our method can significantly improve building model rendering efficiency for large-scale 3D city visualization.

scholarly journals HOLISTIC PARAMETRIC RECONSTRUCTION OF BUILDING MODELS FROM POINT CLOUDS

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 689-695
Author(s):  
Z. Li ◽  
W. Zhang ◽  
J. Shan
Keyword(s):  
Large Scale ◽  
Traditional Approach ◽  
Holistic Approach ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Reconstruction Method ◽  
Optimization Strategy ◽  
Building Roof ◽  
Building Model ◽  
Building Models

Abstract. Building models are conventionally reconstructed by building roof points via planar segmentation and then using a topology graph to group the planes together. Roof edges and vertices are then mathematically represented by intersecting segmented planes. Technically, such solution is based on sequential local fitting, i.e., the entire data of one building are not simultaneously participating in determining the building model. As a consequence, the solution is lack of topological integrity and geometric rigor. Fundamentally different from this traditional approach, we propose a holistic parametric reconstruction method which means taking into consideration the entire point clouds of one building simultaneously. In our work, building models are reconstructed from predefined parametric (roof) primitives. We first use a well-designed deep neural network to segment and identify primitives in the given building point clouds. A holistic optimization strategy is then introduced to simultaneously determine the parameters of a segmented primitive. In the last step, the optimal parameters are used to generate a watertight building model in CityGML format. The airborne LiDAR dataset RoofN3D with predefined roof types is used for our test. It is shown that PointNet++ applied to the entire dataset can achieve an accuracy of 83% for primitive classification. For a subset of 910 buildings in RoofN3D, the holistic approach is then used to determine the parameters of primitives and reconstruct the buildings. The achieved overall quality of reconstruction is 0.08 meters for point-surface-distance or 0.7 times RMSE of the input LiDAR points. This study demonstrates the efficiency and capability of the proposed approach and its potential to handle large scale urban point clouds.

Use of topographic LiDAR point clouds for building reconstruction

2021 ◽  
Author(s):  
Yipeng Yuan
Keyword(s):  
Large Scale ◽  
Three Dimensional ◽  
Point Clouds ◽  
Building Roof ◽  
Building Model ◽  
Building Models ◽  
Urban Models ◽  
Elevation Data ◽  
3D Building Model ◽  
Building Reconstruction

Demand for three-dimensional (3D) urban models keeps growing in various civil and military applications. Topographic LiDAR systems are capable of acquiring elevation data directly over terrain features. However, the task of creating a large-scale virtual environment still remains a time-consuming and manual work. In this thesis a method for 3D building reconstruction, consisting of building roof detection, roof outline extraction and regularization, and 3D building model generation, directly from LiDAR point clouds is developed. In the proposed approach, a new algorithm called Gaussian Markov Random Field (GMRF) and Markov Chain Monte Carlo (MCMC) is used to segment point clouds for building roof detection. The modified convex hull (MCH) algorithm is used for the extraction of roof outlines followed by the regularization of the extracted outlines using the modified hierarchical regularization algorithm. Finally, 3D building models are generated in an ArcGIS environment. The results obtained demonstrate the effectiveness and satisfactory accuracy of the developed method.

scholarly journals Automatic 3-D Building Model Reconstruction from Very High Resolution Stereo Satellite Imagery

2019 ◽  
Vol 11 (14) ◽  
pp. 1660
Author(s):  
Partovi ◽  
Fraundorfer ◽  
Bahmanyar ◽  
Huang ◽  
Reinartz
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Large Scale ◽  
Missing Values ◽  
Model Reconstruction ◽  
Large Area ◽  
Building Model ◽  
Building Models ◽  
D Model ◽  
Very High

Recent advances in the availability of very high-resolution (VHR) satellite data together withefficient data acquisition and large area coverage have led to an upward trend in their applicationsfor automatic 3-D building model reconstruction which require large-scale and frequent updates,such as disaster monitoring and urban management. Digital Surface Models (DSMs) generatedfrom stereo satellite imagery suffer from mismatches, missing values, or blunders, resulting inrough building shape representations. To handle 3-D building model reconstruction using suchlow-quality DSMs, we propose a novel automatic multistage hybrid method using DSMs togetherwith orthorectified panchromatic (PAN) and pansharpened data (PS) of multispectral (MS) satelliteimagery. The algorithm consists of multiple steps including building boundary extraction anddecomposition, image-based roof type classification, and initial roof parameter computation whichare prior knowledge for the 3-D model fitting step. To fit 3-D models to the normalized DSM(nDSM) and to select the best one, a parameter optimization method based on exhaustive searchis used sequentially in 2-D and 3-D. Finally, the neighboring building models in a building blockare intersected to reconstruct the 3-D model of connecting roofs. All corresponding experimentsare conducted on a dataset including four different areas of Munich city containing 208 buildingswith different degrees of complexity. The results are evaluated both qualitatively and quantitatively.According to the results, the proposed approach can reliably reconstruct 3-D building models, eventhe complex ones with several inner yards and multiple orientations. Furthermore, the proposedapproach provides a high level of automation by limiting the number of primitive roof types and byperforming automatic parameter initialization.

scholarly journals Parameter-Free Half-Spaces Based 3D Building Reconstruction Using Ground and Segmented Building Points from Airborne LiDAR Data with 2D Outlines

2021 ◽  
Vol 13 (21) ◽  
pp. 4430
Author(s):  
Marko Bizjak ◽  
Borut Žalik ◽  
Niko Lukač
Keyword(s):  
Large Scale ◽  
Airborne Lidar ◽  
Lidar Data ◽  
New Approach ◽  
Building Model ◽  
Building Models ◽  
3D Building Model ◽  
Two Stages ◽  
Building Level ◽  
Airborne Lidar Data

This paper aims to automatically reconstruct 3D building models on a large scale using a new approach on the basis of half-spaces, while making no assumptions about the building layout and keeping the number of input parameters to a minimum. The proposed algorithm is performed in two stages. First, the airborne LiDAR data and buildings’ outlines are preprocessed to generate buildings’ base models and the corresponding half-spaces. In the second stage, the half-spaces are analysed and used for shaping the final 3D building model using 3D Boolean operations. In experiments, the proposed algorithm was applied on a large scale, and its’ performance was inspected on a city level and on a single building level. Accurate reconstruction of buildings with various layouts were demonstrated and limitations were identified for large-scale applications. Finally, the proposed algorithm was validated on an ISPRS benchmark dataset, where a RMSE of 1.31 m and completeness of 98.9 % were obtained.

scholarly journals ESTABLISHING A NATIONAL 3D GEO-DATA MODEL FOR BUILDING DATA COMPLIANT TO CITYGML: CASE OF TURKEY

2016 ◽  
Vol XLI-B2 ◽  
pp. 79-86 ◽  
Author(s):  
S. Ates Aydar ◽  
J. Stoter ◽  
H. Ledoux ◽  
E. Demir Ozbek ◽  
T. Yomralioglu
Keyword(s):  
Data Model ◽  
Large Scale ◽  
National Level ◽  
Class Structure ◽  
Building Model ◽  
3D Objects ◽  
Exchange Format ◽  
Building Models ◽  
3D Applications ◽  
Thematic Data

This paper presents the generation of the 3D national building geo-data model of Turkey, which is compatible with the international OGC CityGML Encoding Standard. We prepare an ADE named CityGML-TRKBIS.BI that is produced by extending existing thematic modules of CityGML according to TRKBIS needs. All thematic data groups in TRKBIS geo-data model have been remodelled in order to generate the national large scale 3D geo-data model for Turkey. Specific attention has been paid to data groups that have different class structure according to related CityGML data themes such as building data model. Current 2D geo-information model for building data theme of Turkey (TRKBIS.BI) was established based on INSPIRE specifications for building (Core 2D and Extended 2D profiles), ISO/TC 211 standards and OGC web services. New version of TRKBIS.BI which is established according to semantic and geometric rules of CityGML will represent 2D-2.5D and 3D objects. After a short overview on generic approach, this paper describes extending CityGML building data theme according to TRKBIS.BI through several steps. First, building models of both standards were compared according to their data structure, classes and attributes. Second, CityGML building model was extended with respect to TRKBIS needs and CityGML-TRKBIS Building ADE was established in UML. This study provides new insights into 3D applications in Turkey. The generated 3D geo-data model for building thematic class will be used as a common exchange format that meets 2D, 2.5D and 3D implementation needs at national level.

scholarly journals INTEGRATION OF IMAGES AND LIDAR POINT CLOUDS FOR BUILDING FAÇADE TEXTURING

2016 ◽  
Vol XLI-B2 ◽  
pp. 379-382 ◽  
Author(s):  
L. C. Chen ◽  
L. L. Chan ◽  
W. C. Chang
Keyword(s):  
Texture Mapping ◽  
Point Clouds ◽  
Matrix Analysis ◽  
High Fidelity ◽  
Occlusion Detection ◽  
Grey Level ◽  
Building Models ◽  
Close Range ◽  
Occurrence Matrix ◽  
Selection Of

This paper proposes a model-based method for texture mapping using close-range images and Lidar point clouds. Lidar point clouds are used to aid occlusion detection. For occluded areas, we compensate the occlusion by different view-angle images. Considering the authenticity of façade with repeated patterns under different illumination conditions, a selection of optimum pattern is suggested. In the selection, both geometric shape and texture are analyzed. The grey level co-occurrence matrix analysis is applied for the selection of the optimal façades texture to generate of photorealistic building models. Experimental results show that the proposed method provides high fidelity textures in the generation of photorealistic building models. It is demonstrated that the proposed method is also practical in the selection of the optimal texture.

scholarly journals HYBRID MODELING BASED ON SCSG-BR AND ORTHOPHOTO

2018 ◽  
Vol XLII-3 ◽  
pp. 2499-2503 ◽  
Author(s):  
G. Zhou ◽  
Y. Huang ◽  
T. Yue ◽  
X. Li ◽  
W. Huang ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Texture Mapping ◽  
Remote Sensing Data ◽  
Realistic Model ◽  
Boundary Representation ◽  
Digital City ◽  
Building Model ◽  
Organization And Management ◽  
Urban Buildings ◽  
Building Models

With the development of digital city, digital applications are more and more widespread, while the urban buildings are more complex. Therefore, establishing an effective data model is the key to express urban building models accurately. In addition, the combination of 3D building model and remote sensing data become a trend to build digital city there are a large amount of data resulting in data redundancy. In order to solve the limitation of single modelling of constructive solid geometry (CSG), this paper presents a mixed modelling method based on SCSG-BR for urban buildings representation. On one hand, the improved CSG method, which is called as “Spatial CSG (SCSG)” representation method, is used to represent the exterior shape of urban buildings. On the other hand, the boundary representation (BR) method represents the topological relationship between geometric elements of urban building, in which the textures is considered as the attribute data of the wall and the roof of urban building. What's more, the method combined file database and relational database is used to manage the data of three-dimensional building model, which can decrease the complex processes in texture mapping. During the data processing, the least-squares algorithm with constraints is used to orthogonalize the building polygons and adjust the polygons topology to ensure the accuracy of the modelling data. Finally, this paper matches the urban building model with the corresponding orthophoto. This paper selects data of Denver, Colorado, USA to establish urban building realistic model. The results show that the SCSG-BR method can represent the topological relations of building more precisely. The organization and management of urban building model data reduce the redundancy of data and improve modelling speed. The combination of orthophoto and urban building model further strengthens the application in view analysis and spatial query, which enhance the scope of digital city applications.

scholarly journals Automatic, Multiview, Coplanar Extraction for CityGML Building Model Texture Mapping

2021 ◽  
Vol 14 (1) ◽  
pp. 50
Author(s):  
Haiqing He ◽  
Jing Yu ◽  
Penggen Cheng ◽  
Yuqian Wang ◽  
Yufeng Zhu ◽  
...  
Keyword(s):  
Texture Mapping ◽  
3D Models ◽  
Building Model ◽  
Texture Information ◽  
Building Models ◽  
Photogrammetric Method ◽  
Data Source ◽  
Single Data Source ◽  
Single Data ◽  
Point Line

Most 3D CityGML building models in street-view maps (e.g., Google, Baidu) lack texture information, which is generally used to reconstruct real-scene 3D models by photogrammetric techniques, such as unmanned aerial vehicle (UAV) mapping. However, due to its simplified building model and inaccurate location information, the commonly used photogrammetric method using a single data source cannot satisfy the requirement of texture mapping for the CityGML building model. Furthermore, a single data source usually suffers from several problems, such as object occlusion. We proposed a novel approach to achieve CityGML building model texture mapping by multiview coplanar extraction from UAV remotely sensed or terrestrial images to alleviate these problems. We utilized a deep convolutional neural network to filter out object occlusion (e.g., pedestrians, vehicles, and trees) and obtain building-texture distribution. Point-line-based features are extracted to characterize multiview coplanar textures in 2D space under the constraint of a homography matrix, and geometric topology is subsequently conducted to optimize the boundary of textures by using a strategy combining Hough-transform and iterative least-squares methods. Experimental results show that the proposed approach enables texture mapping for building façades to use 2D terrestrial images without the requirement of exterior orientation information; that is, different from the photogrammetric method, a collinear equation is not an essential part to capture texture information. In addition, the proposed approach can significantly eliminate blurred and distorted textures of building models, so it is suitable for automatic and rapid texture updates.

scholarly journals INTEGRATION OF IMAGES AND LIDAR POINT CLOUDS FOR BUILDING FAÇADE TEXTURING

2016 ◽  
Vol XLI-B2 ◽  
pp. 379-382
Author(s):  
L. C. Chen ◽  
L. L. Chan ◽  
W. C. Chang
Keyword(s):  
Texture Mapping ◽  
Point Clouds ◽  
Matrix Analysis ◽  
High Fidelity ◽  
Occlusion Detection ◽  
Grey Level ◽  
Building Models ◽  
Close Range ◽  
Occurrence Matrix ◽  
Selection Of

This paper proposes a model-based method for texture mapping using close-range images and Lidar point clouds. Lidar point clouds are used to aid occlusion detection. For occluded areas, we compensate the occlusion by different view-angle images. Considering the authenticity of façade with repeated patterns under different illumination conditions, a selection of optimum pattern is suggested. In the selection, both geometric shape and texture are analyzed. The grey level co-occurrence matrix analysis is applied for the selection of the optimal façades texture to generate of photorealistic building models. Experimental results show that the proposed method provides high fidelity textures in the generation of photorealistic building models. It is demonstrated that the proposed method is also practical in the selection of the optimal texture.

Fragility Evaluation in Building-Piping Systems: Effect of Piping Interaction With Buildings

2018 ◽  
Vol 141 (1) ◽  
Author(s):  
Yong Hee Ryu ◽  
Abhinav Gupta ◽  
Bu Seog Ju
Keyword(s):  
Large Scale ◽  
Nonlinear Models ◽  
Time History ◽  
Piping System ◽  
Nonstructural Components ◽  
Building Model ◽  
Building Models ◽  
Piping Systems ◽  
Story Building ◽  
Critical Locations

Many studies assessing the damage from 1971 San Fernando and 1994 North Ridge earthquakes reported that the failure of nonstructural components like piping systems was one of the significant reasons for shutdown of hospitals immediately after the earthquakes. This paper is focused on evaluating seismic fragility of a large-scale piping system in representative high-rise, midrise, and low-rise buildings using nonlinear time history analyses. The emphasis is on evaluating piping's interaction with building and its effect on piping fragility. The building models include the effects of nonlinearity in the performance of beams and columns. In a 20-story building that is detuned with the piping system, critical locations are on the top two floors for the linear frame building model. For the nonlinear building model, critical locations are on the bottom two floors. In an eight-story building that is nearly tuned with the piping system, the critical locations for both the linear frame and nonlinear models are the third and fourth floors. It is observed that building nonlinearity can reduce fragility due to reduction in the tuning between building and piping systems. In a two-story building, the nonlinear building frequencies are closer to the critical piping system frequencies than the linear building frequency; the nonlinear building is more fragile than the linear building for this case. However, it is observed that the linear building models give excessively conservative estimates of fragility than the nonlinear building models.

Sign in / Sign up

Export Citation Format

Share Document