RESEARCH ON 3D BUILDING VISUALIZATION BASED ON TEXTURE SIMPLIFICATION AND FRACTAL COMPRESSION

3D modeling of buildings is an important method in mapping and modeling the built environment. In this study, we analyzed the differences between the semantic state of actual buildings and 3D models of LoD3 buildings generated using aerial and terrestrial photogrammetric methods. We also evaluated the accuracy of the visual representation as well as the suitability of the building geometry and texture. Our method involves collecting aerial and terrestrial photographic data and processing it using SFM (structure from motion) technology. The photogrammetric data was then processed using image matching algorithms and 3D reconstruction techniques to generate 3D models of LoD3 buildings. The actual semantic state of the building was identified through field surveys and reference data collection. The 3D building model was successfully modeled from 1201 photos and 19 ground control points. The results of the evaluation of the geometry accuracy test, dimensions and semantic completeness of the 3D model, the use of aerial photographs and terrestrial photogrammetry in LoD3 3D modeling are assessed from the results of the automatic 3D modeling process using SfM (Structure from Motion) technology that produces 3D building models in Level of Detail (LoD) 3 with Root Mean Square Error values <0.5 meters and has semantic completeness of the building in accordance with the original object based on the City Geography Markup Language (CityGML) standard. The facade formed from the modeling almost follows the original model such as doors, windows, hallways, etc.

PurposeThe current technique used to measure construction is the conventional total station method. However, the conventional method is time-consuming and could not be used to create a photo-realistic three-dimensional (3D) model of an object. Furthermore, the Canseleri building is located at a slope. The paper aims to discuss this issue.Design/methodology/approachThe aim of this study is to assess the geometric accuracy of a 3D model using unmanned aerial vehicle (UAV) images. There are two objectives in this study. The first is to construct a 3D model of the Canseleri building using UAV images. The second objective is to validate the 3D model of the Canseleri building based on actual measurements.FindingsThe close-range photogrammetry method, using the UAV platform, was able to produce a 3D building model. The results show that the errors between the actual measurement and the generated 3D model were less than 4 cm. The accuracy of the 3D model achieved in this study was about 0.015 m, compared to total station measurements.Originality/valueAccuracy assessment was done by comparing the estimated measurement of the 3D model with the direct measurement. The differences between the measured values with actual values could be compared. Based on this study, the 3D building model gave a reliable accuracy for specific applications.

Abstract. With recent government initiatives for smart cities, 3D virtual city models are in demand for managing and monitoring the urban infrastructure. 3D building models forms an important component of 3D virtual city model. LiDAR remote sensing has revolutionized the way the third dimension can be precisely mapped and proved to be an important source of data for 3D models. The model thus generated should be in an open data format to be used across various applications. CityGML is an open data model framework that enables storage and exchange of 3D models which can be used for diversified applications. The main objective of this research is to develop a methodological workflow to create 3D building models in CityGML standard from airborne LiDAR point cloud. Initially building points were isolated from the airborne LiDAR data using point cloud processing algorithms. 3D building models with levels of detail (LoD1 and LoD2) were generated from the building points in CityGML standard using commercial (ArcGISPro) well as open source packages (3dfier, Citygml4j). Results prove that the models developed using open source packages are comparable to that provided by commercial packages.

Abstract. Photo-realistic 3D model of object cannot be created by using conventional Total Station method. The aim of this study is to generate 3D building model using UAV’s dense point clouds. In this research, the ability of remotely detected information for the construction of 3D building models is found to be as specific as UAV datasets, Tacheometry Survey and Traverse Survey. Pix4D and MeshLab software were used in this study to perform image processing of 3D building. The building was built up upon using the information needed from capturing UAV images. This procedure requires the height of building to be measured through conventional method such that of UAV and the height is then compared with a 3D building model. At the end of the process, the comparison of height of building, also known as RMSE, was conducted. The images of building were then processed in Photogrammetry which is the Pix4D software. Using Pix4D, this study proceeded with several processing stages such as camera calibration, photo uploading, building of dense cloud, mesh generation, and model texturing. Those processes were done automatically by using Pix4D tools. High quality of photo-realistic 3D model of building was then successfully constructed. The accuracy assessment was done by comparing the measurement of 3D model of building. Direct measuring of building by using total station had taken place to compare the measurements between 3D model details with actual measurements. The measurement of 3D model of building was done in Pix4D software. The RMSE achieved in this study is 0.015 m which is under reasonable accuracy limit. This study proves that UAV point cloud is capable to produce a very fine 3D model.

Abstract. 3D city models are increasingly being used to represent the complexity of today’s urban areas, as they aid in understanding how different aspects of a city can function. For instance, several municipalities and governmental organisations have constructed their 3D city models for various purposes. These 3D models, which are normally complex and contain semantics information, have typically been used for visualisation and visual analysis purposes. However, most of the available 3D models open datasets contain many geometric and topological errors, e.g., missing surfaces (holes), self-intersecting surfaces, duplicate vertices, etc. These errors prevent the datasets from being used for advanced applications such as 3D spatial analysis which requires valid datasets and topology to calculate its volume, detect surface orientation, area calculation, etc. Therefore, certain repairs must be done before taking these models into actual applications, and hole-filling (of missing surfaces) is an important one among them. Several studies on the topic of automatic repair of the 3D model have been conducted by various researchers, with different approaches have been developed. Thus, this paper describes a triangular mesh approach for automatically repair invalid (missing surfaces) 3D building model (LOD2). The developed approach demonstrates an ability to repair missing surfaces (with holes) in a 3D building model by reconstructing geometries of the holes of the affected model. The repaired model is validated and produced a closed-two manifold model.

This paper proposes a method of generating 3D building models with precise geospatial information and a photograph-based facade appearance from panoramic image sequences and digital maps. 3D building modeling research is actively being conducted in areas such as geographic information systems, virtual reality, and augmented reality. However, the generation of realistic 3D models from a ground-level viewpoint is still extremely costly in terms of labor of modeling experts, and collection of data. We have developed a method for 3D building modeling with high-resolution photograph-based appearance information using panoramic images captured at ground level with a mobile mapping system, and geospatial information obtained from a digital map. The proposed method includes 1) pre-processing for tilt correction and base 3D model generation, 2) geo-registration of panoramic images with minimal user input, and 3) building height and shape estimation. This paper presents the proposed method and the quantitative performance measure obtained from a developed test modeling system. In addition, modeling results from an experimental dataset are also presented.

The applications of 3D building models are limited as producing them requires massive labor and time costs as well as expensive devices. In this paper, we aim to propose a novel and web-based interactive platform, VGI3D, to overcome these challenges. The platform is designed to reconstruct 3D building models by using free images from internet users or volunteered geographic information (VGI) platform, even though not all these images are of high quality. Our interactive platform can effectively obtain each 3D building model from images in 30 seconds, with the help of user interaction module and convolutional neural network (CNN). The user interaction module provides the boundary of building facades for 3D building modeling. And this CNN can detect facade elements even though multiple architectural styles and complex scenes are within the images. Moreover, user interaction module is designed as simple as possible to make it easier to use for both of expert and non-expert users. Meanwhile, we conducted a usability testing and collected feedback from participants to better optimize platform and user experience. In general, the usage of VGI data reduces labor and device costs, and CNN simplifies the process of elements extraction in 3D building modeling. Hence, our proposed platform offers a promising solution to the 3D modeling community.

The simplification of three‐dimensional (3D) building models to improve rendering efficiency has gained widespread attention. To maintain the model's overall appearance features while increasing the simplification rate, we propose a novel 3D building simplification method that considers both the model mesh and building structure. The method divides a 3D building into a primary structure and subsidiary structures. It then organizes these structures using StructureTree, a multi‐way tree. The structures are organized according to the dependency relationships between building structures. When simplifying a building, the decision whether to simplify the mesh or remove the subsidiary structure in the leaf node of the StructureTree depends on the volume change caused by the edge collapse and the visual saliency of the removed structure. The experimental results show that our method exhibits a better simplification effect than the traditional simplification method, and the proposed method can achieve a high simplification rate while maintaining the simplification quality. Furthermore, the results of some spatial analyses based on the highly simplified building model are consistent with those of the original model.

Interoperability and data exchange within BIM platform to evaluate building energy performance and indoor comfort

The essential techniques used in 3D model reconstruction are LIDAR scanning, close-range photogrammetry, and aerial photogrammetry. The aim of this study is to investigate 3D building model results using a UAV platform using different altitudes and ranges. The methodology was divided into four phases. Phase one was a preliminary study and phase two was the data acquisition phase which included flight planning, camera calibration, and image acquisition at three different altitudes; 150m, 170m, and 190m and at different ranges; 180m, 190m, and 200m, respectively. Phase three was data processing whereby the resulting 3D models were generated using five steps which are image masking, image aligning, image dense clouding, image meshing, and image texturing. Phase four contained analyses on the 3D model such as accuracy assessment of the 3D model and analysis on the different altitudes and ranges employed. This study stated that the maximum accuracy was 0.195m without a control point, while the minimum accuracy was 0.123m with a control point. It shows that the 3D model with a control point produced measurements that were 37% more accurate than the 3D model without a control point. In the final analysis, the results showed that the built 3D model resulting from the UAV platform can provide an accuracy of less than 0.200m with or without control points. These findings can be applied to many fields, such as heritage and conservation in the efforts to preserve the 3D model of heritage objects.

Component Identification and Interpretation

Flood simulation modelling and analysis has been an important research area owing to the impact, the natural disaster can cause to humans and man-made resources. Accurate models and better visualisation of the flooding scenario are gaining prominence to improve the disaster preparedness and hence reduce the human and economic loss. Geo-spatial technologies have played a critical role in developing accurate flood simulation models over the years. Particularly precise elevation information obtained from LiDAR sensors have improved the flood simulation models. This work focuses on using 3D models derived airborne LiDAR point cloud over Vaihingen, Germany for flood simulation modelling. Initially 3D building models were developed in open source CityGML format from airborne LiDAR data. Flood simulation is demonstrated using open source HEC-RAS software incorporating high resolution elevation models derived from LiDAR. The methodological framework incorporates 3D building models with flood flow analysis using HEC-RAS software to demonstrate the flood simulation at different discharge levels. Web based platform was developed to visualize the flooding scenario for better governance.

Nowadays, 3D city models are used by the increasing number of applications. Most applications require not only geometric information but also semantic information. As a standard and tool for 3D city model, CityGML, provides a method for storing and managing both geometric and semantic information. Moreover, it also provides the multi-scale representation of 3D building model for efficient visualization. In CityGML, building models are represented in five LODs (Level of Detail), start from LOD0, LOD1, LOD2, LOD3, and LOD4. Each level has different accuracy and detail requirement for visualization. Usually, for obtaining multi-LOD of 3D building model, several data sources are integrated. For example, LiDAR data is used for generating LOD0, LOD1, and LOD2 as close-range photogrammetry data is used for generating more detailed model in LOD3 and LOD4. However, using additional data sources is increasing cost and time consuming. Since the development of TLS (Terrestrial Laser Scanner), data collection for detailed model can be conducted in a relative short time compared to photogrammetry. Point cloud generated from TLS can be used for generating the multi-LOD of building model. This paper gives an overview about the representation of 3D building model in CityGML and also method for generating multi-LOD of building from TLS data. An experiment was conducted using TLS. Following the standard in CityGML, point clouds from TLS were processed resulting 3D model of building in different level of details. Afterward, models in different LOD were converted into XML schema to be used in CityGML. From the experiment, final result shows that TLS can be used for generating 3D models of building in LOD1, LOD2, and LOD3.

Abstract. Buildings are the most important objects in urban areas. Thus, building detection using photogrammetry and remote sensing data as well as 3D model of buildings are very useful for many applications such as mobile navigation, tourism, and disaster management. In this paper, an approach has been proposed for detecting buildings by LiDAR data and aerial images, as well as reconstructing 3D model of buildings. In this regard, firstly, building detection carried out by utilizing a Supper Vector Machine (SVM) as a supervise method. The supervise methods need training data that could be collected from some features. Hence, LiDAR data and aerial images were utilized to produce some features. The features were selected by considering their abilities for separating buildings from other objects. The evaluation results of building detection showed high accuracy and precision of the utilized approach. The detected buildings were labeled in order to reconstruct buildings, individually. Then the planes of each building were separated and adjacent planes were recognized to reduce the calculation volume and to increase the accuracy. Subsequently, the bottom planes of each building were detected in order to compute the corners of hipped roofs using intersection of three adjacent planes. Also, the corners of gabled roofs were computed by both calculating the intersection line of the adjacent planes and finding the intersection between the planes intersection line and their detected parcel. Finally, the coordinates of some nodes in building floor were computed and 3D model reconstruction was carried out. In order to evaluate the proposed method, 3D model of some buildings with different complexity level were generated. The evaluation results showed that the proposed method has reached credible performance.

Recently three-dimensional (3D) graphics has been applied in areas such as building and mechanical design, with the help of CAD tools to first construct the two-dimensional (2D) and 3D models. Building synthetic environments usually involves lots of work in constructing virtual reality worlds, and making authoring more efficient is one of the most important goals in virtual reality. We propose a prototype system that integrates several modules such as image preprocessing, vectorization, editing, and 3D model generation, to transform a 2D architecture design diagram into a 3D building model. We also propose an improved method that can efficiently recognize slant lines in addition to vertical and horizontal lines. To do this, since a line has a constant slope, we use back-tracking in our method and utilize a line slope consistency check to prune wrong targets.