Generating High Fidelity 3-D Thermograms With a Handheld Real-Time Thermal Imaging System

Infrared thermography is a widely used technique to measure and portray the surface temperature of an object in form of thermal images. Two-dimensional images, however, have some inherent limitations with regard to the fidelity with which they can depict the surface temperature of a three dimensional object. In the past two decades, there have been several works describing different techniques to generate 3-D models textured with thermal information using various combinations of sensors in order to address some of these limitations. Most of these approaches generate 3-D thermograms of an object from a single perspective with bulky measurement systems and therefore do not address problems that arise when scanning objects in a continuous manner from multiple perspectives. But reductions in cost, size, and weight of infrared and depth-sensing cameras as well as a significant increase in computational power of personal computers have enabled the development of low cost, handheld, real-time 3-D thermal imaging systems. This paper elaborates through a series of experiments on the main factors that affect the real-time generation of 3-D thermograms with such a system and demonstrates how taking these factors into consideration significantly improves the appearance and fidelity of the generated 3-D thermogram. Most of the insight gained in this paper can be transferred to 3-D thermal imaging systems based on other combination of sensors.

[1]  Yazhu Chen,et al.  Design of a 3-D Infrared Imaging System Using Structured Light , 2011, IEEE Transactions on Instrumentation and Measurement.

[2]  John J. Leonard,et al.  Kintinuous: Spatially Extended KinectFusion , 2012, AAAI 2012.

[3]  Uwe Stilla,et al.  Matching of 3D building models with IR images for texture extraction , 2011, 2011 Joint Urban Remote Sensing Event.

[4]  Samuel Soldan,et al.  3D Thermal Imaging: Fusion of Thermography and Depth Cameras , 2014 .

[5]  Uwe Stilla,et al.  Thermal leakage detection on building facades using infrared textures generated by mobile mapping , 2009, 2009 Joint Urban Remote Sensing Event.

[6]  Diego González-Aguilera,et al.  Novel approach to 3D thermography and energy efficiency evaluation , 2012 .

[7]  Elisabetta Rosina,et al.  Mapping Infrared Data on Terrestrial Laser Scanning 3D Models of Buildings , 2011, Remote. Sens..

[8]  I. Grubisic,et al.  Medical 3D thermography system , 2011 .

[9]  Francesca Uccheddu,et al.  Automated multispectral texture mapping of 3D models , 2009, 2009 17th European Signal Processing Conference.

[10]  Andreas Nüchter,et al.  The Project ThermalMapper - Thermal 3D Mapping of Indoor Environments for Saving Energy , 2012, SyRoCo.

[11]  R. Du,et al.  Acquisition of 3D surface temperature distribution of a car body , 2005, 2005 IEEE International Conference on Information Acquisition.

[12]  Camino de Vera,et al.  INTEGRATION OF 3D LASER SCANNING, PHOTOGRAMMETRY AND THERMOGRAPHY TO RECORD ARCHITECTURAL MONUMENTS , 2009 .

[13]  Jean-Christophe Nebel,et al.  3D thermography imaging standardization technique for inflammation diagnosis , 2005, SPIE/COS Photonics Asia.

[14]  Peyman Moghadam,et al.  HeatWave : a handheld 3D thermography system for energy auditing , 2013 .

[15]  Youngjib Ham,et al.  An automated vision-based method for rapid 3D energy performance modeling of existing buildings using thermal and digital imagery , 2013, Adv. Eng. Informatics.

[16]  J. Armesto,et al.  Energy efficiency studies through 3D laser scanning and thermographic technologies , 2011 .

[17]  Pedro Arias,et al.  Thermographic 3D models as the foundation for Building Information Models , 2012 .

[18]  Michael Vollmer and Klaus Peter Mollmann,et al.  Infrared thermal imaging , 2018 .

[19]  Andreas Kroll,et al.  On the temperature assignment problem and the use of confidence textures in the creation of 3D thermograms , 2015, 2015 9th International Conference on Sensing Technology (ICST).

[20]  John J. Leonard,et al.  Deformation-based loop closure for large scale dense RGB-D SLAM , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[21]  Jean-José Orteu,et al.  An Innovative Method for 3-D Shape, Strain and Temperature Full-Field Measurement Using a Single Type of Camera: Principle and Preliminary Results , 2008 .

[22]  Surya Prakash,et al.  3D Mapping of Surface Temperature Using Thermal Stereo , 2006, 2006 9th International Conference on Control, Automation, Robotics and Vision.

[23]  M. Pinar Mengüç,et al.  Thermal Radiation Heat Transfer , 2020 .

[24]  Andrew W. Fitzgibbon,et al.  KinectFusion: real-time dynamic 3D surface reconstruction and interaction , 2011, SIGGRAPH '11.

[25]  I. Grubisic,et al.  4D thermal imaging system for medical applications , 2011 .

[26]  Sander Oude Elberink,et al.  Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications , 2012, Sensors.