Towards automatic reconstruction of indoor scenes from incomplete point clouds: door and window detection and regularization

In the last years, point clouds have become the main source of information for building modelling. Although a considerable amount of methodologies addressing the automated generation of 3D models from point clouds have been developed, indoor modelling is still a challenging task due to complex building layouts and the high presence of severe clutters and occlusions. Most of methodologies are highly dependent on data quality, often producing irregular and non-consistent models. Although manmade environments generally exhibit some regularities, they are not commonly considered. This paper presents an optimization-based approach for detecting regularities (i.e., same shape, same alignment and same spacing) in building indoor features. The methodology starts from the detection of openings based on a voxel-based visibility analysis to distinguish ‘occluded’ from ‘empty’ regions in wall surfaces. The extraction of regular patterns in windows is addressed from studying the point cloud from an outdoor perspective. The layout is regularized by minimizing deformations while respecting the detected constraints. The methodology applies for elements placed in the same planeXunta de Galicia | Ref. ED481B 2016/079-

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

Blending Learning and Inference in Structured Prediction

In this paper we derive an efficient algorithm to learn the parameters of structured predictors in general graphical models. This algorithm blends the learning and inference tasks, which results in a significant speedup over traditional approaches, such as conditional random fields and structured support vector machines. For this purpose we utilize the structures of the predictors to describe a low dimensional structured prediction task which encourages local consistencies within the different structures while learning the parameters of the model. Convexity of the learning task provides the means to enforce the consistencies between the different parts. The inference-learning blending algorithm that we propose is guaranteed to converge to the optimum of the low dimensional primal and dual programs. Unlike many of the existing approaches, the inference-learning blending allows us to learn efficiently high-order graphical models, over regions of any size, and very large number of parameters. We demonstrate the effectiveness of our approach, while presenting state-of-the-art results in stereo estimation, semantic segmentation, shape reconstruction, and indoor scene understanding

TOWARDS AUTOMATIC RECONSTRUCTION OF INDOOR SCENES FROM INCOMPLETE POINT CLOUDS: DOOR AND WINDOW DETECTION AND REGULARIZATION

In the last years, point clouds have become the main source of information for building modelling. Although a considerable amount of methodologies addressing the automated generation of 3D models from point clouds have been developed, indoor modelling is still a challenging task due to complex building layouts and the high presence of severe clutters and occlusions. Most of methodologies are highly dependent on data quality, often producing irregular and non-consistent models. Although manmade environments generally exhibit some regularities, they are not commonly considered. This paper presents an optimization-based approach for detecting regularities (i.e., same shape, same alignment and same spacing) in building indoor features. The methodology starts from the detection of openings based on a voxel-based visibility analysis to distinguish ‘occluded’ from ‘empty’ regions in wall surfaces. The extraction of regular patterns in windows is addressed from studying the point cloud from an outdoor perspective. The layout is regularized by minimizing deformations while respecting the detected constraints. The methodology applies for elements placed in the same plane