A 3D Surface Data Model for Fast Visualization of 3DCM

Developing a three-dimensional (3D) data model for Geographic Information Systems (GIS) is an essential and complex issue. 3D modelling in GIS is becoming ever more important for the development of cyber cities and digital earth, which have recently become feasible. A competent 3D model forms an efficient foundation for 3D visualization, query and spatial analysis. As a development of the existing 3D models, this study proposes particular improvements in handling complex 3D objects. We present an object-oriented data model for handling complex 3D objects in GIS. First, the conceptual data model is developed based on the principle of object-oriented (OO) data modelling. This model is designed based on the following three basic geometric elements: node, segment and triangle. Accordingly, the abstract geometric objects are defined: including points, lines, surfaces and volumes. Second, the corresponding 3D logical model is designed based on the defined abstract objects and the relationships between them. Third, a formal representation of the 3D spatial objects is described in detail. Fourth, a prototype 3D GIS is developed based on the proposed 3D data model. Finally, we describe the results of an experimental study to reconstruct 3D objects using this 3D GIS and a comparison with the performance of other 3D data models. The proposed model is able to handle complex objects, such as complex buildings and TV towers, which is an essential functionality for building large-scale cyber cities, such as for Hong Kong. The proposed data model proves to be very efficient, particularly in visualization and rendering. The experimental results show which the data volume of the proposed model is compacted and the visualization speed for 3D objects is improved, compared with the existing models.

Mappings of the earth surface and their representation in 3D (three‐dimensional) models are commonly used in most recent research. Modeling research, which starts with classical surveying methods, acquires new dimensions matching the modern technologies. 3D models of any object or earth surface can be used in much visual and scientific research. A digital model of the landscape is an important part within creation of geo‐information systems used in the public administration and in the commercial sphere. It is an important tool in applications such as geomorphology, hydrology, geology, cartography, ecology, mining etc.Values of volume in terrains that do not have regular geometric structure can be obtained more accurately by using 3D models of surfaces with respect to developing technology. Basic data of 3D models must indicate 3D coordinates of the surveyed object in the reference frame. Distribution and intensity of points are important factors in modeling earth surfaces. A minimum number of points is desired in defining an object in 3D. Interpolation methods employing different mathematical models are used to obtain 3D models of terrain surfaces. In this study, the effect of interpolation methods in defining a terrain surface is investigated. For this purpose, a uniform surface, hill‐shaped artificial object with a known volume is employed. The 3D surface and volume are calculated by using 12 different interpolation methods. Point distribution, point intensity and accuracy of point measurements are not considered. The same data set was used for all the interpolation methods. The interpolation methods are compared and evaluated based on the results. Copyright © 2007 John Wiley & Sons, Ltd.

This chapter presents the implementation of a system that visualizes the construction process using 3D modeling data and schedule data to analyze construction planning. Previous papers have emphasized the benefits of visual 4D planning that combines 3D modeling data and process schedule data for work progress control. The proposed methodology offers rapid visualization of work performance with scheduled activity and facilitates construction planning and schedule inspection. Consequently, it should increase productivity and reduce rework. However, even major construction companies will not adopt such a work style, because the existing, well-organized way of working would not be readily changed unless the new style is proven to afford benefits that outweigh the effort and cost required to adapt to the style. The advanced CAD system, for example, is able to simulate the assembly process, and the advanced 3D graphic designer is able to animate the arrangement of objects. Even though each software provides multiple functionalities, the applications in practical use are all independent and specific, such as CAD for designing 3D models, and a project manager for scheduling and analyzing. Therefore, a system that integrates all outputs from each application is required to move from the conventional work style to the new one. This chapter, thus, aims to develop a system that integrates several types of data and enables the simulation of the construction progress by gradually showing 3D models according to the activity schedule. It is also possible to attach material data to each object and to display related information like cost and object properties. The system assumes the following requirements: (1) to import and display the 3D modeling data, (2) to import the project schedule, (3) to link each model and activity, (4) to give the material data on each object to enhance reality, and (5) to show cost accumulation. These functions are supposed to be realized such that the system utilizes the resources previously reserved. Therefore, the system should be able to import a DXF format file for 3D modeling data and access the MDB format database for the project schedule, including costs. The MDB file is originally a database that Microsoft Access creates. Microsoft Project, which is probably the most widely used software for project management, is also able to export the project data in this format. These functions are implemented with Microsoft Visual C++ and DirectX SDK. Although the system displays inaccurate models partially because of the misinterpretation of the DXF file, all of the demands listed above are satisfied currently. The authors of this chapter are now at the stage of implementation of further functions, that is, to display not only structures but also other elements such as the temporal space on the site, the route of delivery vehicles, and the work area of the temporally used heavy machinery, all for the sake of the visualization and analysis of the entire construction site.

The 3D image construction for the 2D images is a longstanding problem, explored by the number of computer graphics, computer vision, and machine learning research organizations from decades. After the evolution of deep learning architectures, scholars have shown their interest in developing 3D images from the 2D greyscale or RGB images because this perspective shows a significant influence in the discipline of computer vision. The applications of this conversion found in various fields of medical image analysis, robotic vision, game design, lunar explorations, 3D modelling, military, geographical structuring, physics, support models, etc. Potentially, when a 2D image converted to 3D representation, the same image can be viewed from different angles and directions. The 3D structure, which in turn generated, is much more informative than 2D images as it contains information about distance from the camera to a particular object. In this paper, we discussed various exiting methods for generating 3D representations from 2D images, using 3D representational data as well as without 3D representational data and proposing a novel approach for the construction of 3D models from existing 2D images using GAN. Generative adversarial networks (GANs) have shown tremendous results in generating new fake data from existing data, where we cannot detect the false data. Various other architectures of GAN, like HOLO-GAN, IG-GAN, have also been proposed to meet the need to convert 2D to 3D representation, which produced excellent results. After analysing, we provide an extensive comparative review on methods and architectures, which can convert 2D images to 3D objects and express our thoughts on the ideas proposed. Further, the concept of GAN extended to represent 360 view images, panorama images in 3D structures, which plays a vital role in spherical view analysis and synthesis, virtual reality design, augmented reality design, 3D modelling of data, etc.

As assyriologists have to handle tremendous amounts of important documents of ancient history written in cuneiform script, like the epic of Gilgamesh, we are developing an efficent system to support their daily tasks. Previous projects demonstrated the application of holography and early close-range 3D scanners for this task. Based on experiences from our previous projects in archaeology, we are focusing on processing the vast amounts of data of high resolution 3D models from todays close-range 3D scanners like the Breuckmann smartSCAN-3D-HE. The presented method exploits the high-resolution of the 3D models to extract the impressed characters as well as other features like fingerprints. Previous work typically used rendering techniques from computer graphics to visualize the characters, which then had to be processed manually. More recent approaches use methods from differential geometry for detection and extraction of coarse contour lines. These methods are computationally fast, and well-established for industrial application, but cannot cover the variations of human handwriting in form of the – wedge shaped – cuneiform script. To overcome the variations in size of the wedges, we choose a multiscale approach using integrating geometry. A transformation invariant function is achieved by calculating the volumes of multiple concentric spheres intersecting the volume below the 3D model's surface at each point. Due to this multiscale approach, this function is represented by the so-called feature vector. By classifying these feature vectors using auto-correlation, our system – called GigaMesh – can automatically extract characters, requiring only one parameter: the approximated line (wedge) width in mm. Results are shown for cuneiform tablets from the collections of the Assyriologie Heidelberg as well as from the Uruk-Warka Sammlung. Finally an outlook regarding character (en)coding and integration into related projects like the Cuneiform Digital Library Initiative (CDLI) is given.

A state-of-the-art review of automated extraction of rock mass discontinuity characteristics using three-dimensional surface models

With the rise of meta-universe, the application of 3D models in various fields has garnered significant attention, for they may furnish more vivid and detailed representations. However, many problems lie in securing the transmission of 3D model data, as the tampered possibility increases while in transit. Therefore, protecting 3D model data is crucial. Meanwhile, existing chaotic systems have many drawbacks such as degeneracy, predictability, and initial value insensitivity, resulting in a high risk of attacks on 3D model encryption algorithms. This study proposes a 3D model encryption algorithm using a 4D non-degenerate chaotic system and adaptive diffusion based on the finite field. Firstly, a 4-dimensional (4D) non-degenerate chaotic system is designed for generating pseudorandom sequences. Compared to the classical 4D chaotic systems, our proposed chaotic system exhibits better chaotic performance in several aspects, such as Lyapunov exponents, NIST test, and initial value sensitivity. Further, the 3D data format of the 3D model is transformed into a 2D data format. Then, a rotation misalignment across rows and columns (RMARC) is presented to confuse the 2D coordinate data format and break the high correlation of the 2D coordinate values. Finally, the scrambled data is diffused within a finite field using the chaotic sequence to get the diffused 3D model. In addition, we have done many experiments to verify the encryption efficiency, security performance, and encryption quality. The experimental results show that the 3D encryption system performs well in these aspects.

Humans spend most of their life in indoor spaces. As indoor spaces are becoming increasingly complex, there are compelling needs for efficient indoor GIS and navigation systems. For indoor navigations, numerous geometric network models have been proposed as navigable spatial models for 3D indoor environments in the past decade. Most of the existing discussions, however, tend to focus on conceptual representations of geometric networks; not enough attention has been given on the generation processes of navigable networks for 3D indoor environments. It is actually nontrivial, considering accurate and complete floor plans, the conventional data sources for building indoor geometric networks, are oftentimes not available for various reasons (e.g., copyright, public safety concerns). With the continue advances of 3D imaging and scanning technologies, 3D data models with fine geometric structures and high quality textures are increasingly available for indoor spaces, thus provide a novel data source for building indoor geometric networks. In this paper, an interactive approach is presented to derive 3D, navigable, geometric network models from these 3D data models. Specifically, this approach includes three steps: decomposing 3D building models in terms of floors, interactively creating geometric network elements (e.g., nodes and edges) and then automatically generating geometric network models. The presented approach is implemented and its advantages are demonstrated with a real world 3D building data.

Funding Acknowledgements Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Japan Organization of Occupational Health and Safety onbehalf J3D Introduction Improvements in cardiovascular imaging have contributed to the accurate diagnosis and treatment of cardiovascular diseases. In recent years, we can find a new demand from surgical departments, i.e., 3D heart models of patients for preoperative simulations. However, a 3D surgical model needs a detailed segmentation of heart structures from clinical images because these details are essential for achieving accurate polygon data of an exact 3D model. Thus, in most cases, manual segmentation is required for complicated heart shapes such as the chamber wall, valves, and papillary muscles, which causes a prolonged duration time. Purpose We aim to achieve an automated heart segmentation using a convolutional neural network (CNN) trained using deep learning techniques for the rapid creation of 3D surgical heart models of individual patients. Methods We constructed our original CNN program based on the latest artificial intelligence techniques and trained it to extract shapes of the heart from cardiac computed tomography (CT) images. The training data was 361 slices selected from CT scans of 10 patients. We used data augmentation to increase the amount and diversity of the data into 24,052 slices. The training result with the best Intersection over Union (IoU), one of the evaluation metrics used in deep learning, was saved. Finally, we used the best-trained CNN to construct 3D polygon data of two surgical cases of hypertrophic obstructive cardiomyopathy (HOCM) for preoperative assessments. Results The IoU attained 0.85 after deep learning. The time required to complete the 3D polygon data for the first HOCM case was 5 minutes for segmentation by the trained CNN and 3 hours for data correction by a human operator. Similarly, the time required for the second case was 5 minutes for segmentation without manual correction. We had to correct the segmentation for the first case because we needed an exact 3D model for the preoperative assessment (Fig. 1). According to our records of the other eight 3D heart models in the lab, the work for a 3D polygon shape from CT images needs a median 30 hours (quartiles 23-50 hours) when the procedure is fully manual and non-continuous with breaks in between. Conclusion The CNN-based segmentation aided the constructing heart shapes from cardiac CT images of preoperative patients. Although the performance, reaching IoU of 0.85, was insufficient for fully automatic segmentation, the methodology can shorten the process duration from several hours to several minutes for detailed segmentation of heart structures. We previously applied the CNN-based segmentation to the aorta, aortic stenosis valves, and atheromatous plaque in clinical images, demonstrating adequate segmentation performance. The proposed methodology can be applied as a fundamental technology of cardiovascular imaging for obtaining the actual structures of a target object as 3D coordinate values or a 3D model within a reasonable duration time. Abstract Figure 1

Three-dimensional (3D) subsurface modelling and visualization of seven regional unconformity-bounded stratigraphic units, (synthems) of the Messinian-Quaternary succession of the Po Plain Basin and Savigliano-Alessandria Basin are here described. 3D model is developed using the EnterVol Ctech® software environment, grounding on information stored in GeoPiemonte Map database. This modelling allows to visualise the geological data and to establish topological relationships between the analysed objects, coupling the data processing capabilities of GIS with 3D modeling and sharing the result on 3D WebGIS service of ARPA Piemonte Geoportal.

With the rapid development of smart cities, intelligent navigation, and autonomous driving, how to quickly obtain 3D spatial information of urban buildings and build a high-precision 3D fine model has become a key problem to be solved. As the two-dimensional mapping results have constrained various needs in people’s social life, coupled with the concept of digital city and advocacy, making three-dimensional, virtualization and actualization become the common pursuit of people’s goals. However, the original point cloud obtained is always incomplete due to reasons such as occlusion during acquisition and data density decreasing with distance, resulting in extracted boundaries that are often incomplete as well. In this paper, based on the study of current mainstream 3D model data organization methods, geographic grids and map service specifications, and other related technologies, an intelligent urban 3D modeling model based on multisource data point cloud algorithm is designed for the two problems of unified organization and expression of urban multisource 3D model data. A point cloud preprocessing process is also designed: point cloud noise reduction and downsampling to ensure the original point cloud geometry structure remain unchanged, while improving the point cloud quality and reducing the number of point clouds. By outputting to a common 3D format, the 3D model constructed in this paper can be applied to many fields such as urban planning and design, architectural landscape design, urban management, emergency disaster relief, environmental protection, and virtual tourism.

3D inversion and modeling of magnetic and gravimetric data characterizing the geophysical anomaly source in Pratinha I in the southeast of Brazil

This article first generalizes the basic engineering phases of modern rapid prototyping processes, and then describes the techniques of data capture for data modeling and model making. The article also provides a brief overview of the photogrametric techniques of restitution of 3D objects, and highlights the difficulties and limitations of existing methods. It therefore presents a novel approach to photo-modeling for acquiring 3D model data from single 2D photorealistic images. Implementation of the approach is then described against a background of rapid prototyping processes to demonstrate the effectiveness of photo-modeling practice.

The use of volume data and digital three-dimensional (3D) surface models in biology has increased quickly and steadily. Various methods are available to acquire 3D data, among them episcopic imaging techniques. Based on the episcopic microscopy with on-block staining protocol of Weninger et al. (Anat Embryol 197:341–348, 1998), we describe a simple and versatile setup for episcopic microtomy. It is composed of a consumer DSLR digital camera combined with standard histology equipment. The workflow of block surface staining and imaging, image processing, stack alignment, surface generation (including a custom Amira® macro), and 3D model editing is described in detail. For our sample specimen (Alytes obstetricans; Amphibia: Anura) we obtained images with a pixel size of 5.67 × 5.67 µm2. The generated image stacks allowed distinguishing different tissues and were well-suited for creating a 3D surface model. We analyzed the alignment quality achieved by various selections of specimen and fiducial marker spots. The fiducial spots had a significant positive effect on the alignment quality with the best alignment having a maximum mean alignment error of about 44.7 µm. We further tested the APS-C camera with combinations of macro lens, extension tube or teleconverter. The macro lens and extension tube yielded the smallest pixel size of 2.53 × 2.53 µm2. Considering data quality and resolution, and depending on object sizes and research goals, DSLR captured episcopic microtomy can be an alternative to other techniques, such as traditional histological sectioning or micro-computed tomography.

The subject of the investigation reported in this paper is the visualization of three-dimensional (3D) surface models in a 3D mapping survey using terrestrial laser scanning (TLS). The Cangkuang Temple nearby Garut City was chosen as the object. The laser technology instruments used were a Topcon GLS-1000 (Geodetic Laser Scanner) and a Topcon IS (Image Station). Twelve points at the same position in each 3D surface model were selected. The coordinate system of the IS was then transformed into the coordinate system of the GLS-1000 using a 3D similarity transformation model. The 3D distances were calculated for each model. Differences in distance were considered as errors in the x, y, and z direction. The standard deviation of the distance differences was ±0.301 m. Some of the distance differences did not fall within the range of tolerances (about 15%). The 3D surface model visualization of the Cangkuang Temple that was created from the GLS-1000 data was more precise than the one created from the IS data. In the future, such 3D surface model visualizations could be used for documentation, preservation and reconstruction of heritage buildings.