Adaptive Methods for Point Cloud and Mesh Processing

Point clouds and 3D meshes are widely used in numerous applications ranging from games to virtual reality to autonomous vehicles. This dissertation proposes several approaches for noise removal and calibration of noisy point cloud data and 3D mesh sharpening methods. Order statistic filters have been proven to be very successful in image processing and other domains as well. Different variations of order statistics filters originally proposed for image processing are extended to point cloud filtering in this dissertation. A brand-new adaptive vector median is proposed in this dissertation for removing noise and outliers from noisy point cloud data. The major contributions of this research lie in four aspects: 1) Four order statistic algorithms are extended, and one adaptive filtering method is proposed for the noisy point cloud with improved results such as preserving significant features. These methods are applied to standard models as well as synthetic models, and real scenes, 2) A hardware acceleration of the proposed method using Microsoft parallel pattern library for filtering point clouds is implemented using multicore processors, 3) A new method for aerial LIDAR data filtering is proposed. The objective is to develop a method to enable automatic extraction of ground points from aerial LIDAR data with minimal human intervention, and 4) A novel method for mesh color sharpening using the discrete Laplace-Beltrami operator is proposed. Median and order statistics-based filters are widely used in signal processing and image processing because they can easily remove outlier noise and preserve important features. This dissertation demonstrates a wide range of results with median filter, vector median filter, fuzzy vector median filter, adaptive mean, adaptive median, and adaptive vector median filter on point cloud data. The experiments show that large-scale noise is removed while preserving important features of the point cloud with reasonable computation time. Quantitative criteria (e.g., complexity, Hausdorff distance, and the root mean squared error (RMSE)), as well as qualitative criteria (e.g., the perceived visual quality of the processed point cloud), are employed to assess the performance of the filters in various cases corrupted by different noisy models. The adaptive vector median is further optimized for denoising or ground filtering aerial LIDAR data point cloud. The adaptive vector median is also accelerated on multi-core CPUs using Microsoft Parallel Patterns Library. In addition, this dissertation presents a new method for mesh color sharpening using the discrete Laplace-Beltrami operator, which is an approximation of second order derivatives on irregular 3D meshes. The one-ring neighborhood is utilized to compute the Laplace-Beltrami operator. The color for each vertex is updated by adding the Laplace-Beltrami operator of the vertex color weighted by a factor to its original value. Different discretizations of the Laplace-Beltrami operator have been proposed for geometrical processing of 3D meshes. This work utilizes several discretizations of the Laplace-Beltrami operator for sharpening 3D mesh colors and compares their performance. Experimental results demonstrated the effectiveness of the proposed algorithms

Delaunay-kolmioinnin hyödyntäminen infrastruktuurin suunnitteluohjelmistoissa

In Finland, irregular triangulation has traditionally been used in infrastructural design software, such as road, railroad, bridge, tunnel and environmental design software, to model ground surfaces. Elsewhere, methods like regular square and triangle network, approximating surface without a surface presentation, and algebraic surfaces, have been used for the same task. Approximating the ground surface is necessary for tasks such as determining the height of a point on the ground, interpolating 2D polylines onto the ground, calculating height lines, calculating volumes and visualization. In most of these cases, a continuous surface representation, a digital terrain model is needed. Delaunay triangulation is a way of forming an irregular triangulation out of a 2D point set, in such a way that the triangles are well-formed. Well-formed triangles are essential for the accuracy of the surface representation. This Master's Thesis studies how much time and memory it takes to form a Delaunay triangulation for large point sets, and how Delaunay triangulation compares to other methods of forming a surface representation. In addition, the run-time and accuracy of the resulting surface representations is studied in different interpolation and volume calculation tasks.Infrastruktuurin suunnitteluohjelmistoissa, kuten tien-, rautatien-, sillan-, tunnelin-, ja ympäristönsuunnitteluohjelmistoissa, on Suomessa perinteisesti käytetty maaston pinnan mallintamiseen mittapisteistä muodostettua epäsäännöllistä kolmioverkkoa. Muualla maailmassa ovat käytössä olleet säännölliset neliö- ja kolmioverkot, maaston approksimointi ilman pintaesitystä, sekä joissain tapauksissa algebralliset pintaesitykset. Pinnan approksimaatiota tarvitaan em. sovelluksissa mm. pisteen korkeuden arviointiin, 2-ulotteisten murtoviivojen interpolointiin maaston pinnalle, korkeuskäyrien laskemiseen ja massan (tilavuuden) laskentaan annetuilta alueilta sekä visualisointiin. Delaunay-kolmiointi on tapa muodosta 2-ulotteisesta pistejoukosta epäsäännöllinen kolmioverkko, jonka kolmiot hyvin tasamuotoisia. Kolmioiden tasamuotoisuus on oleellisesta pintamallin tarkkuudelle. Tässä työssä tutkitaan Delaunay-kolmioinnin käytettävyyttä maaston mallintamiseen suurilla pistejoukoilla, sekä epäsäännöllisen kolmioinnin käytettävyyttä em. tehtäviin. Työssä vertaillaan Delaunay-kolmioinnin muodostamisen ajan ja muistin kulutusta pintaesityksen muodostamiseen muilla menetelmillä. Lisäksi tutkitaan näin muodostettujen pintamallien tilavuuslaskennan ja interpolaation nopeutta ja tarkkuutta

Regular Hierarchical Surface Models: A conceptual model of scale variation in a GIS and its application to hydrological geomorphometry

Environmental and geographical process models inevitably involve parameters that vary spatially. One example is hydrological modelling, where parameters derived from the shape of the ground such as flow direction and flow accumulation are used to describe the spatial complexity of drainage networks. One way of handling such parameters is by using a Digital Elevation Model (DEM), such modelling is the basis of the science of geomorphometry. A frequently ignored but inescapable challenge when modellers work with DEMs is the effect of scale and geometry on the model outputs. Many parameters vary with scale as much as they vary with position. Modelling variability with scale is necessary to simplify and generalise surfaces, and desirable to accurately reconcile model components that are measured at different scales. This thesis develops a surface model that is optimised to represent scale in environmental models. A Regular Hierarchical Surface Model (RHSM) is developed that employs a regular tessellation of space and scale that forms a self-similar regular hierarchy, and incorporates Level Of Detail (LOD) ideas from computer graphics. Following convention from systems science, the proposed model is described in its conceptual, mathematical, and computational forms. The RHSM development was informed by a categorisation of Geographical Information Science (GISc) surfaces within a cohesive framework of geometry, structure, interpolation, and data model. The positioning of the RHSM within this broader framework made it easier to adapt algorithms designed for other surface models to conform to the new model. The RHSM has an implicit data model that utilises a variation of Middleton and Sivaswamy (2001)’s intrinsically hierarchical Hexagonal Image Processing referencing system, which is here generalised for rectangular and triangular geometries. The RHSM provides a simple framework to form a pyramid of coarser values in a process characterised as a scaling function. In addition, variable density realisations of the hierarchical representation can be generated by defining an error value and decision rule to select the coarsest appropriate scale for a given region to satisfy the modeller’s intentions. The RHSM is assessed using adaptions of the geomorphometric algorithms flow direction and flow accumulation. The effects of scale and geometry on the anistropy and accuracy of model results are analysed on dispersive and concentrative cones, and Light Detection And Ranging (LiDAR) derived surfaces of the urban area of Dunedin, New Zealand. The RHSM modelling process revealed aspects of the algorithms not obvious within a single geometry, such as, the influence of node geometry on flow direction results, and a conceptual weakness of flow accumulation algorithms on dispersive surfaces that causes asymmetrical results. In addition, comparison of algorithm behaviour between geometries undermined the hypothesis that variance of cell cross section with direction is important for conversion of cell accumulations to point values. The ability to analyse algorithms for scale and geometry and adapt algorithms within a cohesive conceptual framework offers deeper insight into algorithm behaviour than previously achieved. The deconstruction of algorithms into geometry neutral forms and the application of scaling functions are important contributions to the understanding of spatial parameters within GISc

Consistent Density Scanning and Information Extraction From Point Clouds of Building Interiors

Over the last decade, 3D range scanning systems have improved considerably enabling the designers to capture large and complex domains such as building interiors. The captured point cloud is processed to extract specific Building Information Models, where the main research challenge is to simultaneously handle huge and cohesive point clouds representing multiple objects, occluded features and vast geometric diversity. These domain characteristics increase the data complexities and thus make it difficult to extract accurate information models from the captured point clouds. The research work presented in this thesis improves the information extraction pipeline with the development of novel algorithms for consistent density scanning and information extraction automation for building interiors. A restricted density-based, scan planning methodology computes the number of scans to cover large linear domains while ensuring desired data density and reducing rigorous post-processing of data sets. The research work further develops effective algorithms to transform the captured data into information models in terms of domain features (layouts), meaningful data clusters (segmented data) and specific shape attributes (occluded boundaries) having better practical utility. Initially, a direct point-based simplification and layout extraction algorithm is presented that can handle the cohesive point clouds by adaptive simplification and an accurate layout extraction approach without generating an intermediate model. Further, three information extraction algorithms are presented that transforms point clouds into meaningful clusters. The novelty of these algorithms lies in the fact that they work directly on point clouds by exploiting their inherent characteristic. First a rapid data clustering algorithm is presented to quickly identify objects in the scanned scene using a robust hue, saturation and value (H S V) color model for better scene understanding. A hierarchical clustering algorithm is developed to handle the vast geometric diversity ranging from planar walls to complex freeform objects. The shape adaptive parameters help to segment planar as well as complex interiors whereas combining color and geometry based segmentation criterion improves clustering reliability and identifies unique clusters from geometrically similar regions. Finally, a progressive scan line based, side-ratio constraint algorithm is presented to identify occluded boundary data points by investigating their spatial discontinuity

Toward Controllable and Robust Surface Reconstruction from Spatial Curves

Reconstructing surface from a set of spatial curves is a fundamental problem in computer graphics and computational geometry. It often arises in many applications across various disciplines, such as industrial prototyping, artistic design and biomedical imaging. While the problem has been widely studied for years, challenges remain for handling different type of curve inputs while satisfying various constraints. We study studied three related computational tasks in this thesis. First, we propose an algorithm for reconstructing multi-labeled material interfaces from cross-sectional curves that allows for explicit topology control. Second, we addressed the consistency restoration, a critical but overlooked problem in applying algorithms of surface reconstruction to real-world cross-sections data. Lastly, we propose the Variational Implicit Point Set Surface which allows us to robustly handle noisy, sparse and non-uniform inputs, such as samples from spatial curves

3D Spatial Data Infrastructures for web-based Visualization

In this thesis, concepts for developing Spatial Data Infrastructures with an emphasis on visualizing 3D landscape and city models in distributed environments are discussed. Spatial Data Infrastructures are important for public authorities in order to perform tasks on a daily basis, and serve as research topic in geo-informatics. Joint initiatives at national and international level exist for harmonizing procedures and technologies. Interoperability is an important aspect in this context - as enabling technology for sharing, distributing, and connecting geospatial data and services. The Open Geospatial Consortium is the main driver for developing international standards in this sector and includes government agencies, universities and private companies in a consensus process. 3D city models are becoming increasingly popular not only in desktop Virtual Reality applications but also for being used in professional purposes by public authorities. Spatial Data Infrastructures focus so far on the storage and exchange of 3D building and elevation data. For efficient streaming and visualization of spatial 3D data in distributed network environments such as the internet, concepts from the area of real time 3D Computer Graphics must be applied and combined with Geographic Information Systems (GIS). For example, scene graph data structures are commonly used for creating complex and dynamic 3D environments for computer games and Virtual Reality applications, but have not been introduced in GIS so far. In this thesis, several aspects of how to create interoperable and service-based environments for 3D spatial data are addressed. These aspects are covered by publications in journals and conference proceedings. The introductory chapter provides a logic succession from geometrical operations for processing raw data, to data integration patterns, to system designs of single components, to service interface descriptions and workflows, and finally to an architecture of a complete distributed service network. Digital Elevation Models are very important in 3D geo-visualization systems. Data structures, methods and processes are described for making them available in service based infrastructures. A specific mesh reduction method is used for generating lower levels of detail from very large point data sets. An integration technique is presented that allows the combination with 2D GIS data such as roads and land use areas. This approach allows using another optimization technique that greatly improves the usability for immersive 3D applications such as pedestrian navigation: flattening road and water surfaces. It is a geometric operation, which uses data structures and algorithms found in numerical simulation software implementing Finite Element Methods. 3D Routing is presented as a typical application scenario for detailed 3D city models. Specific problems such as bridges, overpasses and multilevel networks are addressed and possible solutions described. The integration of routing capabilities in service infrastructures can be accomplished with standards of the Open Geospatial Consortium. An additional service is described for creating 3D networks and for generating 3D routes on the fly. Visualization of indoor routes requires different representation techniques. As server interface for providing access to all 3D data, the Web 3D Service has been used and further developed. Integrating and handling scene graph data is described in order to create rich virtual environments. Coordinate transformations of scene graphs are described in detail, which is an important aspect for ensuring interoperability between systems using different spatial reference systems. The Web 3D Service plays a central part in nearly all experiments that have been carried out. It does not only provide the means for interactive web-visualizations, but also for performing further analyses, accessing detailed feature information, and for automatic content discovery. OpenStreetMap and other worldwide available datasets are used for developing a complete architecture demonstrating the scalability of 3D Spatial Data Infrastructures. Its suitability for creating 3D city models is analyzed, according to requirements set by international standards. A full virtual globe system has been developed based on OpenStreetMap including data processing, database storage, web streaming and a visualization client. Results are discussed and compared to similar approaches within geo-informatics research, clarifying in which application scenarios and under which requirements the approaches in this thesis can be applied

Proceedings of the 8th International Conference on Civil Engineering

This open access book is a collection of accepted papers from the 8th International Conference on Civil Engineering (ICCE2021). Researchers and engineers have discussed and presented around three major topics, i.e., construction and structural mechanics, building materials, and transportation and traffic. The content provide new ideas and practical experiences for both scientists and professionals

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways