Sensor Data Visualization for Indoor Point Clouds

Integration and analysis of real-time and historic sensor data provides important insights into the operational status of buildings. There is a need for the integration of sensor data and digital representations of the built environment for furthering stakeholder engagement within the realms of Real Estate 4.0 and Facility Management (FM), especially in a spatial representation context. In this paper, we propose a general system architecture that integrates point cloud data and sensor data for visualization and analysis. We further present a prototypical web-based implementation of that architecture and demonstrate its application for the integration and visualization of sensor data from a typical office building, with the aim to communicate and analyze occupant comfort. The empirical results obtained from our prototypical implementation demonstrate the feasibility of our approach for the provisioning of light-weight software components for the service-oriented integration of Building Information Modeling (BIM), Building Automation Systems (BASs), Integrated Workplace Management Systems (IWMSs), and future Digital Twin (DT) platforms.

[1]  Jürgen Döllner,et al.  Server-based rendering of large 3D scenes for mobile devices using G-buffer cube maps , 2012, Web3D '12.

[2]  Muhammad Khalid Bashir,et al.  Development of a Building Information Modelling (BIM)-Based Real-Time Data Integration System Using a Building Management System (BMS) , 2017 .

[3]  Juha Hyyppä,et al.  Visualization of building models and sensor data using open 3D platforms , 2016 .

[4]  Norbert Pfeifer,et al.  Georeferenced Point Clouds: A Survey of Features and Point Cloud Management , 2013, ISPRS Int. J. Geo Inf..

[5]  Pierre Alliez,et al.  A Survey of Surface Reconstruction from Point Clouds , 2017, Comput. Graph. Forum.

[6]  Matthias Trapp,et al.  Evaluating the Perceptual Impact of Rendering Techniques on Thematic Color Mappings in 3D Virtual Environments , 2013, VMV.

[7]  Pete Smith,et al.  BIM & the 5D Project Cost Manager , 2014 .

[8]  Burcu Akinci,et al.  Automatic Creation of Semantically Rich 3D Building Models from Laser Scanner Data , 2011 .

[9]  Kasper Hornbæk,et al.  Big data from the built environment , 2011, LARGE '11.

[10]  V. Coors,et al.  LINKING 3D BUILDING MODELS, MAPS AND ENERGY-RELATED DATA IN A WEB-BASED VISUALIZATION SYSTEM , 2018, SDSC 2018.

[11]  Niloy J. Mitra,et al.  Estimating surface normals in noisy point cloud data , 2003, SCG '03.

[12]  Pieter Abbeel,et al.  Image Object Label 3 D CAD Model Candidate Grasps Google Object Recognition Engine Google Cloud Storage Select Feasible Grasp with Highest Success Probability Pose EstimationCamera Robots Cloud 3 D Sensor , 2014 .

[13]  Anh Nguyen,et al.  3D point cloud segmentation: A survey , 2013, 2013 6th IEEE Conference on Robotics, Automation and Mechatronics (RAM).

[14]  Ren-Jye Dzeng,et al.  An Automated IoT Visualization BIM Platform for Decision Support in Facilities Management , 2018, Applied Sciences.

[15]  Gichun Cha,et al.  A study on data visualization of embedded sensors for building energy monitoring using BIM , 2016, International Journal of Precision Engineering and Manufacturing.

[16]  Habibollah Haron,et al.  Surface reconstruction techniques: a review , 2012, Artificial Intelligence Review.

[17]  Timo Ojala,et al.  Comparison of two workflows for Web-based 3D smart home visualizations , 2018, 2018 IEEE International Conference on Future IoT Technologies (Future IoT).

[18]  Markus König,et al.  Evaluation of Open Data Models for the Exchange of Sensor Data in Cognitive Building , 2018, 2018 Workshop on Metrology for Industry 4.0 and IoT.

[19]  Yi Han,et al.  Implementation of Cloud BIM-based Platform Towards High-performance Building Services ☆ , 2017 .

[20]  Yiannis Kompatsiaris,et al.  Deep Learning Advances in Computer Vision with 3D Data , 2017, ACM Comput. Surv..

[21]  Paulo Carreira,et al.  BIMSL: A generic approach to the integration of building information models with real-time sensor data , 2017 .

[22]  Frank Schultmann,et al.  Building Information Modeling (BIM) for existing buildings — Literature review and future needs , 2014 .

[23]  Paul M. Teicholz,et al.  BIM for facility managers , 2013 .

[24]  Alessandra Flammini,et al.  Exploiting Internet of Things and building information modeling framework for management of cognitive buildings , 2016, 2016 IEEE International Smart Cities Conference (ISC2).

[25]  Allison Kealy,et al.  Shape Grammar Approach to 3D Modeling of Indoor Environments Using Point Clouds , 2019, J. Comput. Civ. Eng..

[26]  Karen Kensek.  Integration of Environmental Sensors with BIM: Case studies using Arduino, Dynamo, and the Revit API , 2014 .

[27]  Robert Lark,et al.  The Emulation and Simulation of Internet of Things Devices for Building Information Modelling (BIM) , 2018, EG-ICE.

[28]  Jürgen Döllner,et al.  Concepts and techniques for web-based visualization and processing of massive 3D point clouds with semantics , 2019, Graph. Model..

[29]  Fan Xue.  Understanding unstructured 3D point clouds for creating digital twin city: An unsupervised hierarchical clustering approach , 2019 .

[30]  Jürgen Döllner,et al.  Service-oriented interactive 3D visualization of massive 3D city models on thin clients , 2011, COM.Geo.

[31]  Zeeshan Aziz,et al.  From conventional to IT based visual management: a conceptual discussion for lean construction , 2017, J. Inf. Technol. Constr..

[32]  Sun,et al.  Usage of 3D Point Cloud Data in BIM （Building Information Modelling）： Current Applications and Challenges , 2015 .

[33]  Rokia Raslan,et al.  Case studies of environmental visualization , 2017 .

[34]  Alice Moncaster,et al.  Building Information Modelling, Building Performance, Design and Smart Construction , 2017 .

[35]  Jochen Teizer,et al.  Internet of Things (IoT) for Integrating Environmental and Localization Data in Building Information Modeling (BIM) , 2017 .

[36]  Volker Coors,et al.  INTEGRATION AND VISUALIZATION OF HETEROGENEOUS SENSOR DATA AND GEOSPATIAL INFORMATION , 2018, SDSC 2018.

[37]  Philip Woodall,et al.  Developing a dynamic digital twin at a building level: Using Cambridge campus as case study , 2019 .

[38]  Jan Boehm,et al.  A review on deep learning techniques for 3D sensed data classification , 2019, Remote. Sens..

[39]  Sheryl Staub-French,et al.  Construction Quality Assessment Using 3D as-built Models Generated with Project Tango , 2016 .