Generation of TIR-attributed 3D Point Clouds from UAV-based Thermal Imagery

Westfeld, Patrick; Mader, David; Maas, Hans‐Gerd

doi:10.1127/1432-8364/2015/0274

Cited by 33 publications

(25 citation statements)

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“…The resulted adjusted bundle block delivers corrected orientation parameters that are used for semi-global-matching [29]. Semi global matching 10.21611/qirt.2016.042 is, dependant on the recording configuration, not only applicable for RGB images [30], but also for TIR images [19]. The GPS/INS parameters used as input values for the bundle adjustment can be seen as accurate enough to assume that both th RGB point cloud and the TIR point cloud are very close together in object space and have almost the same scale.…”

Section: Coregistration Of Rgb and Tir Point Cloudmentioning

confidence: 99%

“…A geometric calibration including principal point, focal length, and radial distortion parameters has been investigated by some groups [16,17]. 3d reconstruction and texture extraction in thermal infrared are applied for sets of images and ordered terrestrial image sequences [18] or image sequences taken by a thermal camera mounted on a RPAS [19]. Both 3d reconstruction and texturing are influenced by various conditions as the thermal radiation of facades depends on temperature differences between inside and outside, weather conditions, and materials.…”

Section: Introductionmentioning

confidence: 99%

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Automatic 3D reconstruction and texture extraction for 3D building models from thermal infrared image sequences.

Hoegner¹,

Stilla²

2016

Proceedings of the 2016 International Conference on Quantitative InfraRed Thermography

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This paper discusses the accuracies of two methods for automatic image orientation, 3D reconstruction, and extraction of textures of building facades from thermal IR image sequences. The first method uses the image sequence and camera calibration information only to reconstruct the scene in model coordinates and coregisters this model to a given 3D building model to derive optimized orientation parameters. The second method directly includes the given 3D building model into the bundle adjustment of the image sequence orientation. The resulting optimized orientation parameters are used to project the 3D model into the images and extract façade textures. Both methods are evaluated on their geometric accuracy using experimental image sequences.

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Section: Coregistration Of Rgb and Tir Point Cloudmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic 3D reconstruction and texture extraction for 3D building models from thermal infrared image sequences.

Hoegner¹,

Stilla²

2016

Proceedings of the 2016 International Conference on Quantitative InfraRed Thermography

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show abstract

“…The resulted adjusted bundle block delivers corrected orientation parameters that are used for semiglobal-matching (Hirschmueller, 2008). Semi global matching is, dependant on the recording configuration, not only applicable for RGB images (Mayer et al, 2012), but also for TIR images (Westfeld et al, 2015). The GPS/INS parameters used as input values for the bundle adjustment can be seen as accurate enough to assume that both th RGB point cloud and the TIR point cloud are very close together in object space and have almost the same scale.…”

Section: Coregistration Of 3d Point Cloudsmentioning

confidence: 99%

“…A geometric calibration including principal point, focal length, and radial distortion parameters has been investigated by some groups (Simmler, 2009;Luhmann et al, 2010;Lagela et al, 2011). 3d reconstruction and texture extraction in thermal infrared are applied for sets of images and ordered terrestrial image sequences (Hoegner and Stilla, 2015) or image sequences taken by a thermal camera mounted on a RPAS (Westfeld et al, 2015). Both 3d reconstruction and texturing are influenced by various conditions as the thermal radiation of facades depends on temperature differences between inside and outside, weather conditions, and materials.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Methods for Coregistration and Fusion of Rpas-Based 3d Point Clouds and Thermal Infrared Images

Hoegner¹,

Tuttas²,

Xu³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper discusses the automatic coregistration and fusion of 3d point clouds generated from aerial image sequences and corresponding thermal infrared (TIR) images. Both RGB and TIR images have been taken from a RPAS platform with a predefined flight path where every RGB image has a corresponding TIR image taken from the same position and with the same orientation with respect to the accuracy of the RPAS system and the inertial measurement unit. To remove remaining differences in the exterior orientation, different strategies for coregistering RGB and TIR images are discussed: (i) coregistration based on 2D line segments for every single TIR image and the corresponding RGB image. This method implies a mainly planar scene to avoid mismatches; (ii) coregistration of both the dense 3D point clouds from RGB images and from TIR images by coregistering 2D image projections of both point clouds; (iii) coregistration based on 2D line segments in every single TIR image and 3D line segments extracted from intersections of planes fitted in the segmented dense 3D point cloud; (iv) coregistration of both the dense 3D point clouds from RGB images and from TIR images using both ICP and an adapted version based on corresponding segmented planes; (v) coregistration of both image sets based on point features. The quality is measured by comparing the differences of the back projection of homologous points in both corrected RGB and TIR images.

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“…Visible images are used facade reconstruction (Ripperda and Brenner, 2006), damage assessment (Kerle et al, 2014). Using a thermal camera, the energy efficiency of buildings can be assessed and heat leakages can be detected , Borrmann et al, 2012, Westfeld et al, 2015.…”

Section: Motivationmentioning

confidence: 99%

Quality Assessment of Building Textures Extracted From Oblique Airborne Thermal Imagery

Iwaszczuk¹,

Stilla²

2016

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

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ABSTRACT:Thermal properties of the building hull became an important topic of the last decade. Combining the thermal data with building models makes it possible to analyze thermal data in a 3D scene. In this paper we combine thermal images with 3D building models by texture mapping. We present a method for texture extraction from oblique airborne thermal infrared images. We put emphasis on quality assessment of these textures and evaluation of their usability for thermal inspections. The quality measures used for assessment are divided to resolution, occlusion and matching quality.

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Generation of TIR-attributed 3D Point Clouds from UAV-based Thermal Imagery

Cited by 33 publications

References 0 publications

Automatic 3D reconstruction and texture extraction for 3D building models from thermal infrared image sequences.

Automatic 3D reconstruction and texture extraction for 3D building models from thermal infrared image sequences.

Evaluation of Methods for Coregistration and Fusion of Rpas-Based 3d Point Clouds and Thermal Infrared Images

Quality Assessment of Building Textures Extracted From Oblique Airborne Thermal Imagery

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