Photometric Correction for Infrared Sensors

Jincheng Zhang, Andrew R. Willis, Kevin Brink; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2023, pp. 431-439


Infrared thermography has been widely used in several domains to capture and measure temperature distributions across surfaces and objects. This methodology can be further expanded to 3D applications if the spatial distribution of the temperature distribution is available. Structure from Motion (SfM) is a photometric range imaging technique that makes it possible to obtain 3D renderings from a cloud of 2D images. To explore the possibility of 3D reconstruction via SfM from infrared images, this article proposes a photometric correction model for infrared sensors based on temperature constancy. Photometric correction is accomplished by estimating the scene irradiance as the values from the solution to a differential equation for microbolometer pixel excitation with unknown coefficients and initial conditions. The model was integrated into an SfM framework and experimental evaluations demonstrate the contribution of the photometric correction for improving the estimates of both the camera motion and the scene structure. Further, experiments show that the reconstruction quality from the corrected infrared imagery achieves performance on par with state-of-the-art reconstruction using RGB sensors.

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[pdf] [arXiv]
@InProceedings{Zhang_2023_CVPR, author = {Zhang, Jincheng and Willis, Andrew R. and Brink, Kevin}, title = {Photometric Correction for Infrared Sensors}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {431-439} }