Camera pose refinement by matching uncertain 3D building models with thermal infrared image sequences for high quality texture extraction

<p><strong>Abstract.</strong> In this work, we discussed how to directly combine thermal infrared image (TIR) and the point cloud without additional assistance from GCPs or 3D models. Specifically, we propose a point-based co-registration process for combining the TIR image and the point cloud for the buildings. The keypoints are extracted from images and point clouds via primitive segmentation and corner detection, then pairs of corresponding points are identified manually. After that, the estimated camera pose can be computed with EPnP algorithm. Finally, the point cloud with thermal information provided by IR images can be generated as a result, which is helpful in the tasks such as energy inspection, leakage detection, and abnormal condition monitoring. This paper provides us more insight about the probability and ideas about the combining TIR image and point cloud.</p>

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BUILDING FACADE OBJECT DETECTION FROM TERRESTRIAL THERMAL INFRARED IMAGE SEQUENCES COMBINING DIFFERENT VIEWS

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-ii-3-w4-55-2015 ◽

2015 ◽

Vol II-3/W4 ◽

pp. 55-62 ◽

Cited By ~ 4

Author(s):

L. Hoegner ◽

U. Stilla

Keyword(s):

Object Detection ◽

Infrared Image ◽

Thermal Infrared ◽

Image Sequences ◽

Bundle Adjustment ◽

Adjustment Process ◽

Building Model ◽

Building Facades ◽

Fully Automatic ◽

Orientation Parameters

This paper discusses the automatic texturing of building facades from thermal infrared image sequences. A fully automatic method is presented to refine GPS based positions estimating relative orientations of the image sequences including a given building model in a bundle adjustment process. The resulting refined orientation parameters are used to extract partial facade textures from all images and all sequences. The resulting partial textures of every sequence are combined to get complete facade textures in the thermal infrared domain. Textures from different image sequences are combined for object detection and extraction. These sequences are acquired either at different times for different radiometric thermal behavior of facade objects or with different viewing directions for objects located before or behind the facade plane.

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Line based Matching of Uncertain 3D Building Models with IR Image Sequences for Precise Texture Extraction

Photogrammetrie - Fernerkundung - Geoinformation ◽

10.1127/1432-8364/2012/0135 ◽

2012 ◽

Vol 2012 (5) ◽

pp. 511-521 ◽

Cited By ~ 5

Author(s):

Dorota Iwaszczuk ◽

Ludwig Hoegner ◽

Michael Schmitt ◽

Uwe Stilla

Keyword(s):

Image Sequences ◽

Building Models ◽

Texture Extraction

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Automatic generation of façade textures from terrestrial thermal infrared image sequences.

10.21611/qirt.2014.211 ◽

2014 ◽

Author(s):

L. Hoegner ◽

U. Stilla

Keyword(s):

Infrared Image ◽

Thermal Infrared ◽

Automatic Generation ◽

Image Sequences

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Day and Night Clouds Detection Using a Thermal-Infrared All-Sky-View Camera

Remote Sensing ◽

10.3390/rs13091852 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1852

Author(s):

Yiren Wang ◽

Dong Liu ◽

Wanyi Xie ◽

Ming Yang ◽

Zhenyu Gao ◽

...

Keyword(s):

Infrared Image ◽

Thermal Infrared ◽

Field Of View ◽

Detection Scheme ◽

Detection Approach ◽

Cloud Characteristics ◽

Formation And Evolution ◽

Fish Eye ◽

Infrared Channel

The formation and evolution of clouds are associated with their thermodynamical and microphysical progress. Previous studies have been conducted to collect images using ground-based cloud observation equipment to provide important cloud characteristics information. However, most of this equipment cannot perform continuous observations during the day and night, and their field of view (FOV) is also limited. To address these issues, this work proposes a day and night clouds detection approach integrated into a self-made thermal-infrared (TIR) all-sky-view camera. The TIR camera consists of a high-resolution thermal microbolometer array and a fish-eye lens with a FOV larger than 160°. In addition, a detection scheme was designed to directly subtract the contamination of the atmospheric TIR emission from the entire infrared image of such a large FOV, which was used for cloud recognition. The performance of this scheme was validated by comparing the cloud fractions retrieved from the infrared channel with those from the visible channel and manual observation. The results indicated that the current instrument could obtain accurate cloud fraction from the observed infrared image, and the TIR all-sky-view camera developed in this work exhibits good feasibility for long-term and continuous cloud observation.

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