Reconstrucción digital de estructuras de tejados históricos: desarrollo de un flujo de trabajo de análisis altamente automatizado

[EN] Planning on adaptive reuse, maintenance and restoration of historic timber structuresrequiresextensive architectural and structural analysis of the actual condition. Current methods for a modellingof roof constructions consist of several manual steps including the time-consuming dimensional modelling. The continuous development of terrestrial laser scanners increases the accuracy, comfort and speed of the surveying work inroof constructions. Resultingpoint clouds enabledetailed visualisation of theconstructionsrepresented by single points or polygonal meshes, but in fact donot containinformation about the structural system and the beam elements. The developed workflow containsseveral processing steps on the point cloud dataset. The most important among them arethenormal vector computation, the segmentation of points to extract planarfaces, a classification of planarsegmentsto detect the beam side facesand finally theparametric modelling of the beams on the basis of classified segments. Thisenablesa highly automated transitionfrom raw point cloud data to a geometric model containing beams of the structural system. The geometric model,as well as additional information about the structural properties of involved wooden beams and their joints,is necessaryinput for a furtherstructural modellingof timber constructions. The results of the workflow confirm that the proposed methods work well for beams with a rectangularcross-section and minor deformations. Scan shadows and occlusionof beamsby additional installationsor interlockingbeamsdecreases the modelling performance, but in generala high level ofaccuracy and completeness isachieved ata high degree of automation.[ES] Las estructuras históricas de madera requieren un análisis arquitectónico y estructural exhaustivo de su condición real en aras de planificar la reutilización flexible, el mantenimiento y la restauración. Los métodos actuales que modelan las construcciones de cubiertas pasan por aplicar varias etapas en modo manual, que incluye el lento modelado dimensional. El desarrollo continuo de escáneres láser terrestres aumenta la exactitud, la comodidad y la velocidad del trabajo topográfico en construcciones de tejados. Las nubes de puntos resultantes permiten la visualización detallada de las construcciones representadas por puntos o mallas poligonales, pero de hecho no contienen información sobre el sistema estructural y los elementos del travesaño. El flujo de trabajo desarrollado contiene varias etapas de procesamiento en el conjunto de datos de la nube de puntos. Los más importantes son el cálculo del vector normal, la segmentación de puntos que extraen caras planas, la clasificación de segmentos planos que detectan las caras laterales del travesaño y, finalmente, el modelado paramétrico de los travesaños en función de los segmentos clasificados. Esto permite una transición altamente automatizada de los datos de la nube de puntos brutos a un modelo geométrico que contiene los travesaños del sistema estructural. El modelo geométrico, así como la información adicional sobre las propiedades estructurales de las vigas de madera involucradas y de sus juntas, es información necesaria de entrada para el modelado estructural eventual de las construcciones de madera. Los resultados del flujo de trabajo confirman que los métodos propuestos funcionan bien en travesaños que presentan secciones transversales rectangulares y deformaciones menores. Las sombras en los escaneados y las oclusiones de los travesaños a partir de instalaciones adicionales o vigas entrelazados disminuye el rendimiento del modelado, pero en general se logra un nivel de exactitud e integridad elevado con un alto grado de automatización.Pöchtrager, M.; Styhler-Aydın, G.; Döring-Williams, M.; Pfeifer, N. (2018). Digital reconstruction of historic roof structures: developing a workflow for a highly automated analysis. Virtual Archaeology Review. 9(19):21-33. doi:10.4995/var.2018.8855SWORD2133919Attene, M., & Spagnuolo, M. (2000). Automatic surface reconstruction from point sets in space. Computer Graphics Forum, 19(3), 457-465. doi:10.1111/1467-8659.00438Baik, A., Yaagoubi, R., & Boehm, J. (2015). Integration of Jeddah historical BIM and 3D GIS for documentation and restoration of historical monument. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-5/W7, 29-34. doi:10.5194/isprsarchives-XL-5-W7-29-2015Bassier, M., Hadjidemetriou, G., Vergauwen, M., Van Roy, N., & Verstrynge, E. (2016). Implementation of Scan-to-BIM and FEM for the Documentation and Analysis of Heritage Timber Roof Structures. In M. Ioannides, E. Fink, A. Moropoulou, M. Hagedorn-Saupe, A. Fresa, G. Liestøl, . . . P. Grussenmeyer (Ed.), Digital Heritage. Progress in Cultural Heritage: Documentation, Preservation, and Protection. EuroMed 2016 (pp. 79-90). Springer, Cham. doi:10.1007/978-3-319-48496-9_7Besl, P., & McKay, N. (1992). A method for registration of 3D Shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14, 239-254. doi:10.1109/34.121791Chida, A., & Masuda, H. (2016). Reconstruction of polygonal prisms from point-clouds of engineering facilities. Journal of Computational Design and Engineering, 3(4), 322-329. doi:10.1016/j.jcde.2016.05.003Dore, C., & Murphy, M. (2017). Current state of the art historic building information modelling. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W5, 185-192. doi:10.5194/isprsarchives-XLII-2-W5-185-2017Dorninger, P., Nothegger, C., & Rasztovits, S. (2013). Efficient 3-D documentation of Neptune fountain in the park of Schönbrunn palace at millimeter scale. Proceedings XXIV International CIPA Symposium, ISPRS Annals, II, 5/W1, 103-108. doi:10.5194/isprsannals-II-5-W1-103-2013Eßer, G., Styhler-Aydın, G., & Hochreiner, G. (2016a). Construction history and structural assessment of historic roofs - An interdisciplinary approach. In K. Van Balen, & E. Verstrynge (Eds.), Structural analysis of historical constructions. Anamnesis, diagnosis, therapy, controls (pp. 790-795). London, GB.Eßer, G., Styhler-Aydın, G., & Hochreiner, G. (2016b). The historic roof structures of the Vienna Hofburg: An innovative interdisciplinary approach in architectural sciences laying ground for structural modeling. In J. Eberhardsteiner, W. Winter, A. Fadai, & M. Pöll (Eds.), WCTE 2016. World conference on timber engineering (pp. 3039-3047). Wien, Austria.Fischler, M., & Bolles, R. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 381-395. doi:10.1145/358669.358692Glira, P., Pfeifer, N., Briese, C., & Ressl, C. (2015). A Correspondence Framework for ALS Strip Adjustments based on Variants of the ICP Algorithm. Photogrammetrie, Fernerkundung, Geoinformation, 4, 275-289. doi:10.1127/pfg/2015/0270Hochreiner, G., Eßer, G., & Styhler-Aydın, G. (2016). Modern timber engineering methods in the context of historical timber structures. In J. Eberhardsteiner, W. Winter, A. Fadai, & M. Pöll (Eds.), WCTE 2016. World conference on timber engineering (pp. 4830-4838). Wien, Austria.Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., & Stuetzle, W. (1992). Surface reconstruction from unorganized points. SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques. ACM SIGGRAPH Computer Graphics, 26(2), 71-78. doi:10.1145/142920.134011International Organization for Standardization. (2016). Industrial automation systems and integration -- Product data representation and exchange -- Part 21: Implementation methods: Clear text encoding of the exchange Structure. ISO/DIS Standard No. 10303-21. Retrieved from https://www.iso.org/standard/63141.html.Jung, J., Hong, S., Jeong, S., Kim, S., Cho, H., Hong, S., & Heo, J. (2014). Productive modeling for development of asbuilt BIM of existing indoor structures. Automation in Construction, 42, 68-77. doi:10.1016/j.autcon.2014.02.021Kazhdan, M., Bolitho, M., & Hoppe, H. (2006). Poisson surface reconstruction. Symposium on Geometry Processing (pp. 61-70). The Eurographics Association. doi:10.2312/SGP/SGP06/061-070Lee, J., Son, H., Kim, C., & Kim, C. (2013). Skeleton-based 3-D reconstruction of as-built pipelines from laser-scan data. Automation in Reconstruction, 35, 199-207. doi:10.1061/9780784412343.0031Li, W., Goodchild, M., & Church, R. (2013). An efficient measure of compactness for two-dimensional shapes and its application in regionalization problems. International Journal of Geographical Information Science, 1227-1250. doi:10.1080/13658816.2012.752093Nothegger, C., & Dorninger, P. (2009). 3D filtering of high-resolution terrestrial laser scanner point clouds for cultural heritage documentation. Photogrammetrie, Fernerkundung, Geoinformation, 1, 53-63. doi:10.1127/0935-1221/2009/0006Pfeifer, N., & Winterhalder, D. (2004). Modelling of tree cross sections from terrestrial laser scanning data with free-form curves. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36(8/W2), 76-81.Pfeifer, N., Mandlburger, G., Otepka, J., & Karel, W. (2014). OPALS - A framework for Airborne Laser Scanning data analysis. Computers, Environment and Urban Systems, 45, 125-136. doi:10.1016/j.compenvurbsys.2013.11.002Pöchtrager, M., Styhler-Aydın, G., Döring-Williams, M., & Pfeifer, N. (2017). Automated Reconstruction of Historic Roof Structures from Point Clouds - Development and Examples. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-2-W2, 195-202. doi:10.5194/isprs-annals-IV-2-W2-195-2017Rabbani, T., Dijkman, S., Van den Heuvel, F., & Vosselman, G. (2007). An integrated approach for modelling and global registration of point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 61(6), 355-370. doi:10.1016/j.isprsjprs.2006.09.006Raumonen, P., Kaasalainen, M., Åkerblom, M., Kaasalainen, S., Kaartinen, H., Vastaranta, M., . . . Lewis, P. (2013). Fast automatic precision tree models from terrestrial laser scanner data. Remote Sensing, 5(2), 491-520. doi:10.3390/rs5020491Stylianidis, E., & Remondino, F. (2016). 3D Recording, Documentation and Management of Cultural Heritage. Caithness, UK: Whittles Publishing.Thies, M., Pfeifer, N., Winterhalder, D., & Gorte, B. (2004). Three-dimensional reconstruction of stems for assessment of taper, sweep and lean based on laser scanning of standing trees. Scandinavian Journal of Forest Research, 19(6), 571-581. doi:10.1080/02827580410019562Thomson, C., & Boehm, J. (2015). Automatic geometry generation from point clouds for BIM. Remote Sensing, 7(9), 11753-11775. doi:10.3390/rs70911753Vosselman, G., & Maas, H.-G. (2010). Airborne and Terrestrial Laser Scanning. Caithness, UK: Whittles Publishing.Wang, D., Hollaus, M., Puttonen, E., & Pfeifer, N. (2016). Automatic and self-adaptive stem reconstruction in landslide-affected forests. Remote Sensing, 8(12), p. 974. doi:10.3390/rs8120974Wang, D., Kankare, V., Puttonen, E., Hollaus, M., & Pfeifer, N. (2016). Reconstructing stem cross section shapes from terrestrial laser scanning. IEEE Geoscience and Remote Sensing Letters, 14(2), 272-276. doi:10.1109/LGRS.2016.2638738Xiong, X., Adan, A., Akinci, B., & Huber, D. (2013). Automatic creation of semantically rich 3D building models from laser scanner data. Automation in Construction, 31, S. 325-337. doi:10.1016/j.autcon.2012.10.006Yang, X., Koehl, M., & Grussenmeyer, P. (2017). Parametric modelling of as-built beam framed structure in BIM environment. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W3, 651-657. doi:10.5194/isprs-archives-XLII-2-W3-651-2017Zhang, R., & Zakhor, A. (2014). Automatic identification of window regions on indoor point clouds using LiDAR and cameras. Applications of Computer Vision (WACV), 2014 IEEE Winter Conference, 107-114. doi:10.1109/WACV.2014.683611

On systematic approaches for interpreted information transfer of inspection data from bridge models to structural analysis

In conjunction with the improved methods of monitoring damage and degradation processes, the interest in reliability assessment of reinforced concrete bridges is increasing in recent years. Automated imagebased inspections of the structural surface provide valuable data to extract quantitative information about deteriorations, such as crack patterns. However, the knowledge gain results from processing this information in a structural context, i.e. relating the damage artifacts to building components. This way, transformation to structural analysis is enabled. This approach sets two further requirements: availability of structural bridge information and a standardized storage for interoperability with subsequent analysis tools. Since the involved large datasets are only efficiently processed in an automated manner, the implementation of the complete workflow from damage and building data to structural analysis is targeted in this work. First, domain concepts are derived from the back-end tasks: structural analysis, damage modeling, and life-cycle assessment. The common interoperability format, the Industry Foundation Class (IFC), and processes in these domains are further assessed. The need for usercontrolled interpretation steps is identified and the developed prototype thus allows interaction at subsequent model stages. The latter has the advantage that interpretation steps can be individually separated into either a structural analysis or a damage information model or a combination of both. This approach to damage information processing from the perspective of structural analysis is then validated in different case studies

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

MULTICULTURAL HERITAGES IN A CITY AS PRODUCTIVE TOURISM PLACES

A city can be a place for productive tourism places. Bandung, for example, has become a tourists' city and especially benefited from weekend visitors from Jakarta. Visitors in the city are offered attractions for leisure and consumption. The city's attractions are dominated by heritages: from pattern of street layout to the existence of a distinctive architecture, to the variety of activities to create the city itself as a visitors' experience. These conditions need a deliberate attempt to create the city as multicultural places of consumption for both retail and tourism as part of urban cultural management. This paper examines the creation of the city as a tourism destination. It is argued that changes to the process of capital accumulation in many cities have led to the commodification of place at a local level. Part of this process has been the creation of heritage as a tangible asset and this is linked to changing patterns of consumer retail activity. This paper argued that tourism should not be conceptualized as a distinct activity but rather as a form of consumption in the context of both local and global changes

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 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 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

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

Heritage Patterns—Representative Models

The Heritage Patterns—Representative Models issue of Heritage welcomed twelve articles that discussed traditional and contemporary methodologies, as well as scholars from different backgrounds who intended to seek patterns of tangible heritage and its underlying principles to understand the diversity of heritage approaches. The Special Issue aims to research the patterns in heritage and the underlying rules that define tangible heritage as a universal value in spatial coexistence, economics, urban life, and design via case studies and theoretical proposals that could be implemented in the future. The pattern language and the heritage phenomenon could act as a base of observation to deduct logic and create generative algorithms (generative design); to understand the importance of spatial connection with tangible heritage and urban forms (space syntax, urban morphology, and urban morphometrics) and its visibility; as well as archaeological, architectural, and urban heritage. Based on the UNESCO-ICOMOS doctrines and the examination of morphological regions, urban morphological research and its different layers (urban forms, structural components, built environment, urban tissue, and their interaction) act as a background and foundation for general urban heritage conservation and protection proposals, and also as the base of specific interventions in the built environment caused by natural disasters