Automatic Structural Scene Digitalization

In this paper, we present an automatic system for the analysis and labeling of structural scenes, floor plan drawings in Computer-aided Design (CAD) format. The proposed system applies a fusion strategy to detect and recognize various components of CAD floor plans, such as walls, doors, windows and other ambiguous assets. Technically, a general rule-based filter parsing method is fist adopted to extract effective information from the original floor plan. Then, an image-processing based recovery method is employed to correct information extracted in the first step. Our proposed method is fully automatic and real-time. Such analysis system provides high accuracy and is also evaluated on a public website that, on average, archives more than ten thousands effective uses per day and reaches a relatively high satisfaction rate.Comment: paper submitted to PloS On

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.SCatch: semantic structure for architectural floor plan retrieval

Architects’ daily routine involves working with drawings. They use either a pen or a computer to sketch out their ideas or to do a drawing to scale. We therefore propose the use of a sketch-based approach when using the floor plan repository for queries. This enables the user of the system to sketch a schematic abstraction of a floor plan and search for floor plans that are structurally similar. We also propose the use of a visual query language, and a semantic structure as put forward by Langenhan. An algorithm extracts the semantic structure sketched by the architect on DFKI’s Touch& Write table and compares the structure of the sketch with that of those from the floor plan repository. The a.SCatch system enables the user to access knowledge from past projects easily. Based on CBR strategies and shape detection technologies, a sketch-based retrieval gives access to a semantic floor plan repository. Furthermore, details of a prototypical application which allows semantic structure to be extracted from image data and put into the repository semi-automatically are provided

Walk2Map: Extracting Floor Plans from Indoor Walk Trajectories

Recent years have seen a proliferation of new digital products for the efficient management of indoor spaces, with important applications like emergency management, virtual property showcasing and interior design. These products rely on accurate 3D models of the environments considered, including information on both architectural and non-permanent elements. These models must be created from measured data such as RGB-D images or 3D point clouds, whose capture and consolidation involves lengthy data workflows. This strongly limits the rate at which 3D models can be produced, preventing the adoption of many digital services for indoor space management. We provide an alternative to such data-intensive procedures by presenting Walk2Map, a data-driven approach to generate floor plans only from trajectories of a person walking inside the rooms. Thanks to recent advances in data-driven inertial odometry, such minimalistic input data can be acquired from the IMU readings of consumer-level smartphones, which allows for an effortless and scalable mapping of real-world indoor spaces. Our work is based on learning the latent relation between an indoor walk trajectory and the information represented in a floor plan: interior space footprint, portals, and furniture. We distinguish between recovering area-related (interior footprint, furniture) and wall-related (doors) information and use two different neural architectures for the two tasks: an image-based Encoder-Decoder and a Graph Convolutional Network, respectively. We train our networks using scanned 3D indoor models and apply them in a cascaded fashion on an indoor walk trajectory at inference time. We perform a qualitative and quantitative evaluation using both simulated and measured, real-world trajectories, and compare against a baseline method for image-to-image translation. The experiments confirm the feasibility of our approach.Comment: To be published in Computer Graphics Forum (Proc. Eurographics 2021

The Conservation Assessment: A Proposed Model for Evaluating Museum Environmental Management Needs

Provided to help museums assess environmental needs, identify problems, and implement technical solutions. Also available in Spanish

Walk2Map: Extracting Floor Plans from Indoor Walk Trajectories

Recent years have seen a proliferation of new digital products for the efficient management of indoor spaces, with important applications like emergency management, virtual property showcasing and interior design. While highly innovative and effective, these products rely on accurate 3D models of the environments considered, including information on both architectural and non-permanent elements. These models must be created from measured data such as RGB-D images or 3D point clouds, whose capture and consolidation involves lengthy data workflows. This strongly limits the rate at which 3D models can be produced, preventing the adoption of many digital services for indoor space management. We provide a radical alternative to such data-intensive procedures by presentingWalk2Map, a data-driven approach to generate floor plans only from trajectories of a person walking inside the rooms. Thanks to recent advances in data-driven inertial odometry, such minimalistic input data can be acquired from the IMU readings of consumer-level smartphones, which allows for an effortless and scalable mapping of real-world indoor spaces. Our work is based on learning the latent relation between an indoor walk trajectory and the information represented in a floor plan: interior space footprint, portals, and furniture. We distinguish between recovering area-related (interior footprint, furniture) and wall-related (doors) information and use two different neural architectures for the two tasks: an image-based Encoder-Decoder and a Graph Convolutional Network, respectively. We train our networks using scanned 3D indoor models and apply them in a cascaded fashion on an indoor walk trajectory at inference time. We perform a qualitative and quantitative evaluation using both trajectories simulated from scanned models of interiors and measured, real-world trajectories, and compare against a baseline method for image-to-image translation. The experiments confirm that our technique is viable and allows recovering reliable floor plans from minimal walk trajectory data