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

Automatic normal orientation in point clouds of building interiors

Orienting surface normals correctly and consistently is a fundamental problem in geometry processing. Applications such as visualization, feature detection, and geometry reconstruction often rely on the availability of correctly oriented normals. Many existing approaches for automatic orientation of normals on meshes or point clouds make severe assumptions on the input data or the topology of the underlying object which are not applicable to real-world measurements of urban scenes. In contrast, our approach is specifically tailored to the challenging case of unstructured indoor point cloud scans of multi-story, multi-room buildings. We evaluate the correctness and speed of our approach on multiple real-world point cloud datasets

Detection of Walls, Floors, and Ceilings in Point Cloud Data

This is the author accepted manuscript. The final version is available from the American Society of Civil Engineers via http://dx.doi.org/10.1061/9780784479827.229The successful implementation of Building Information Models (BIMs) for facility management, maintenance and operation is highly dependent on the ability to generate such models for existing assets. Generating such BIMs typically requires laser scanning to acquire point clouds and significant post-processing to register the clouds, replace the points with BIM objects, assign semantic relationships and add any additional properties, such as materials. Several research efforts have attempted to reduce the post-processing manual effort by classifying the structural elements and clutter in isolated rooms. They have not however examined the complexity of a whole building. In this paper, we propose a robust framework that can automatically process the point cloud of an entire building, possibly with multiple floors, and classify the points belonging to floors, walls and ceilings.. We first extract the planar surfaces by segmenting the point cloud, and then we use contextual reasoning, such as height, orientation, relation to other objects, and local statistics like point density in order to classify them into objects. Experiments were conducted on a registered point cloud of an office building. The results indicated that almost all of the walls and floors/ceilings were correctly clustered in the point cloud.The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreements n° 247586 ("BIMAutoGen") and n° 334241 ("INFRASTRUCTUREMODELS")

Dense Piecewise Planar RGB-D SLAM for Indoor Environments

The paper exploits weak Manhattan constraints to parse the structure of indoor environments from RGB-D video sequences in an online setting. We extend the previous approach for single view parsing of indoor scenes to video sequences and formulate the problem of recovering the floor plan of the environment as an optimal labeling problem solved using dynamic programming. The temporal continuity is enforced in a recursive setting, where labeling from previous frames is used as a prior term in the objective function. In addition to recovery of piecewise planar weak Manhattan structure of the extended environment, the orthogonality constraints are also exploited by visual odometry and pose graph optimization. This yields reliable estimates in the presence of large motions and absence of distinctive features to track. We evaluate our method on several challenging indoors sequences demonstrating accurate SLAM and dense mapping of low texture environments. On existing TUM benchmark we achieve competitive results with the alternative approaches which fail in our environments.Comment: International Conference on Intelligent Robots and Systems (IROS) 201

Automatic Reconstruction of Parametric, Volumetric Building Models from 3D Point Clouds

Planning, construction, modification, and analysis of buildings requires means of representing a building's physical structure and related semantics in a meaningful way. With the rise of novel technologies and increasing requirements in the architecture, engineering and construction (AEC) domain, two general concepts for representing buildings have gained particular attention in recent years. First, the concept of Building Information Modeling (BIM) is increasingly used as a modern means for representing and managing a building's as-planned state digitally, including not only a geometric model but also various additional semantic properties. Second, point cloud measurements are now widely used for capturing a building's as-built condition by means of laser scanning techniques. A particular challenge and topic of current research are methods for combining the strengths of both point cloud measurements and Building Information Modeling concepts to quickly obtain accurate building models from measured data. In this thesis, we present our recent approaches to tackle the intermeshed challenges of automated indoor point cloud interpretation using targeted segmentation methods, and the automatic reconstruction of high-level, parametric and volumetric building models as the basis for further usage in BIM scenarios. In contrast to most reconstruction methods available at the time, we fundamentally base our approaches on BIM principles and standards, and overcome critical limitations of previous approaches in order to reconstruct globally plausible, volumetric, and parametric models.Automatische Rekonstruktion von parametrischen, volumetrischen Gebäudemodellen aus 3D Punktwolken Für die Planung, Konstruktion, Modifikation und Analyse von Gebäuden werden Möglichkeiten zur sinnvollen Repräsentation der physischen Gebäudestruktur sowie dazugehöriger Semantik benötigt. Mit dem Aufkommen neuer Technologien und steigenden Anforderungen im Bereich von Architecture, Engineering and Construction (AEC) haben zwei Konzepte für die Repräsentation von Gebäuden in den letzten Jahren besondere Aufmerksamkeit erlangt. Erstens wird das Konzept des Building Information Modeling (BIM) zunehmend als ein modernes Mittel zur digitalen Abbildung und Verwaltung "As-Planned"-Zustands von Gebäuden verwendet, welches nicht nur ein geometrisches Modell sondern auch verschiedene zusätzliche semantische Eigenschaften beinhaltet. Zweitens werden Punktwolkenmessungen inzwischen häufig zur Aufnahme des "As-Built"-Zustands mittels Laser-Scan-Techniken eingesetzt. Eine besondere Herausforderung und Thema aktueller Forschung ist die Entwicklung von Methoden zur Vereinigung der Stärken von Punktwolken und Konzepten des Building Information Modeling um schnell akkurate Gebäudemodelle aus den gemessenen Daten zu erzeugen. In dieser Dissertation präsentieren wir unsere aktuellen Ansätze um die miteinander verwobenen Herausforderungen anzugehen, Punktwolken mithilfe geeigneter Segmentierungsmethoden automatisiert zu interpretieren, sowie hochwertige, parametrische und volumetrische Gebäudemodelle als Basis für die Verwendung im BIM-Umfeld zu rekonstruieren. Im Gegensatz zu den meisten derzeit verfügbaren Rekonstruktionsverfahren basieren unsere Ansätze grundlegend auf Prinzipien und Standards aus dem BIM-Umfeld und überwinden kritische Einschränkungen bisheriger Ansätze um vollständig plausible, volumetrische und parametrische Modelle zu erzeugen.</p

A geometrical-based approach to recognise structure of complex interiors

3D modelling of building interiors has gained a lot of interest recently, specifically since the rise of Building Information Modeling (BIM). A number of methods have been developed in the past, however most of them are limited to modelling non-complex interiors. 3D laser scanners are the preferred sensor to collect the 3D data, however the cost of state-of-the-art laser scanners are prohibitive to many. Other types of sensors could also be used to generate the 3D data but they have limitations especially when dealing with clutter and occlusions. This research has developed a platform to produce 3D modelling of building interiors while adapting a low-cost, low-level laser scanner to generate the 3D interior data. The PreSuRe algorithm developed here, which introduces a new pipeline in modelling building interiors, combines both novel methods and adapts existing approaches to produce the 3D modelling of various interiors, from sparse room to complex interiors with non-ideal geometrical structure, highly cluttered and occluded. This approach has successfully reconstructed the structure of interiors, with above 96% accuracy, even with high amount of noise data and clutter. The time taken to produce the resulting model is almost real-time, compared to existing techniques which may take hours to generate the reconstruction. The produced model is also equipped with semantic information which differentiates the model from a regular 3D CAD drawing and can be use to assist professionals and experts in related fields