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- Title:
Thermal Mapping from Point Clouds to 3D Building Model Facades
- Document type:
- Zeitschriftenaufsatz
- Author(s):
- Biswanath, Manoj Kumar; Hoegner, Ludwig; Stilla, Uwe
- Abstract:
- Thermal inspection of buildings regarding efficient energy use is an increasing need in today’ s energy-demanding world. This paper proposes a framework for mapping temperature attributes from thermal point clouds onto building facades. The goal is to generate thermal textures for three-dimensional (3D) analysis. Classical texture generation methods project facade images directly onto a 3D building model. Due to the limited level of detail of these models, projection errors occur. Therefore, we use point clouds from mobile laser scanning extended by intensities extracted from thermal infrared (TIR) image sequences. We are not using 3D reconstructed point clouds because of the limited geometric density and accuracy of TIR images, which can lead to poor 3D reconstruction. We project these thermal point clouds onto facades using a mapping algorithm. The algorithm uses a nearest neighbor search to find an optimal nearest point with three different approaches: “ Minimize angle to normal” , “ Minimize perpendicular distance to normal” , and “ Minimize only distance” . Instead of interpolation, nearest neighbor is used because it retains the original temperature values. The thermal intensities of the optimal nearest points are weighted by resolution layers and mapped to the facade. The approach “ Minimize perpendicular distance to normal” yields the finest texture resolution at a reasonable processing time. The accuracy of the generated texture is evaluated based on estimating the shift of the window corner points from a ground truth texture. A performance metric root-mean-square deviation (RMSD) value that measures this shift is calculated. In terms of accuracy, the nearest neighbor method outperformed bilinear interpolation and an existing TIR image-based texturing method.
- Keywords:
- LOCenter; LOCTop_Data_Generation_and_Object_Reconstruction; ai4twinning; thermal mapping; point clouds; TIR textures; nearest neighborhood algorithm; thermal images; building models; texels
- Journal title:
- Remote Sensing
- Year:
- 2023
- Journal volume:
- 15
- Year / month:
- 2023-10
- Journal issue:
- 19
- Reviewed:
- ja
- Language:
- en
- Fulltext / DOI:
- doi:10.3390/rs15194830
- WWW:
- https://www.mdpi.com/2072-4292/15/19/4830
- Print-ISSN:
- 2072-4292
- Impact Factor:
- 5.0
- Status:
- Verlagsversion / published
- Submitted:
- 29.07.2023
- Accepted:
- 02.10.2023
- Date of publication:
- 05.10.2023
- Semester:
- SS 23
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