Tracking Changes in Buildings over Time - Fully Automated Reconstruction and Difference Detection of 3d Scan and BIM files

Tamke, Martin; Zwierzycki, Mateusz; Evers, Henrik Leander; Ochmann, Sebastian; Vock, Richard; Wessel, Raoul

doi:10.52842/conf.ecaade.2016.2.643

Cited by 4 publications

(3 citation statements)

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“…Geometry-based change detection methods have been widely studied in recent years (Gu et al, 2019, Radanovic et al, 2021, Huang et al, 2022. A change detection method by calculating the difference between the LiDAR data and the BIM but the system ignored small items such as furniture has been proposed (Tamke et al, 2016). Calculating differences between two registered 3D LiDAR point clouds to detect changes were presented by Nikoohemat et al (2018).…”

Section: Geometry-based Change Detectionmentioning

confidence: 99%

“…Geometry-based change detection methods have also been developed to perform change detection between LiDAR data and 3D models (Tamke et al, 2016, Marani et al, 2016, Tran and Khoshelham, 2019, Koeva et al, 2019. The proposed approaches detect changes by calculating differences between LiDAR data and the 3D model, but they fail to detect moving objects or small items.…”

Section: Introductionmentioning

confidence: 99%

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Entropy-Based Indoor Change Detection Using Lidar Data and a 3d Model

Zhao,

Tomko,

Khoshelham

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Indoor change detection is important for building monitoring, building management and model-based localization and navigation systems because the real building environment may not always be the same as the design model. This paper presents a novel indoor building change detection method based on entropy. A sequence of real LiDAR scans is acquired with a static LiDAR scanner and the pose of the LiDAR scanner for each scan is then estimated. Synthetic LiDAR scans are generated with the pose of the LiDAR scanner using the 3D model. The real LiDAR scans and synthetic LiDAR scans are sliced horizontally with a certain angular interval and the entropy of all slices of LiDAR scans is then calculated. Differenced entropy between two corresponding slices of real LiDAR scans and synthetic LiDAR scans is calculated for the classification of the changes. Each slice of real LiDAR scans will be classified into one of the four categories of changes: unchanged, moving objects, structural change and non-structural change. Experimental results show that unchanged slices and slices containing moving objects can be accurately detected, achieving 100% accuracy while non-structural and structural changes are detected with an accuracy of 98.5% and 86.3% respectively.

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Section: Geometry-based Change Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Entropy-Based Indoor Change Detection Using Lidar Data and a 3d Model

Zhao,

Tomko,

Khoshelham

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…A key factor in successfully implementing these digital solutions is the effective management of data from facilities, which begins with acquiring accurate 3D geometric information. Point clouds, obtained through state-of-the-art field data acquisition systems and LiDAR scanners, have emerged as the prevailing approach for documenting the 3D geometry of existing buildings [ 2 , 3 ]. This method has gained popularity due to its ability to provide precise and comprehensive representations of structures, facilitating the overall DX of the construction sector.…”

Section: Introductionmentioning

confidence: 99%

Automated Point Cloud Registration Approach Optimized for a Stop-and-Go Scanning System

Park,

Ju,

Nguyen

et al. 2023

Sensors

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The latest advances in mobile platforms, such as robots, have enabled the automatic acquisition of full coverage point cloud data from large areas with terrestrial laser scanning. Despite this progress, the crucial post-processing step of registration, which aligns raw point cloud data from separate local coordinate systems into a unified coordinate system, still relies on manual intervention. To address this practical issue, this study presents an automated point cloud registration approach optimized for a stop-and-go scanning system based on a quadruped walking robot. The proposed approach comprises three main phases: perpendicular constrained wall-plane extraction; coarse registration with plane matching using point-to-point displacement calculation; and fine registration with horizontality constrained iterative closest point (ICP). Experimental results indicate that the proposed method successfully achieved automated registration with an accuracy of 0.044 m and a successful scan rate (SSR) of 100% within a time frame of 424.2 s with 18 sets of scan data acquired from the stop-and-go scanning system in a real-world indoor environment. Furthermore, it surpasses conventional approaches, ensuring reliable registration for point cloud pairs with low overlap in specific indoor environmental conditions.

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Parametric point-cloud slicing for facade retrofitting

Bohórquez

Derigent

El-Haouzi

2021

International Journal of Architectural Computing

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This work presents a method for retrieving 3D building contours usable in facade retrofitting projects, which uses a parametric modeling workflow that utilizes a point-cloud slicing method to retrieve such 3D contours. Since current commitments by European governments seek to reduce energy consumption as a means to reduce carbon emissions from building stock by 2050, facade retrofitting appears as an alternative for addressing operational and embedded building emissions. Within such a context, the main contribution of this work consists of a workflow and a 3D reconstruction solution that uses a parametric environment for capturing building topology and bypassing ground-level occlusions. A real case study and a strategy for converting 3D building contours into Industry Foundation Classes entities, directly from the parametric modeling environment, served as a scenario for testing the capabilities of a Grasshopper solution and open new perspectives for this approach.

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Tracking Changes in Buildings over Time - Fully Automated Reconstruction and Difference Detection of 3d Scan and BIM files

Cited by 4 publications

References 1 publication

Entropy-Based Indoor Change Detection Using Lidar Data and a 3d Model

Entropy-Based Indoor Change Detection Using Lidar Data and a 3d Model

Automated Point Cloud Registration Approach Optimized for a Stop-and-Go Scanning System

Parametric point-cloud slicing for facade retrofitting

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