Recovering photometric properties of architectural scenes from photographs
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
A practical analytic model for daylight
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Acquiring the reflectance field of a human face
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
IEEE Computer Graphics and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
What is the Spectral Dimensionality of Illumination Functions in Outdoor Scenes?
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Multiple-cue Illumination Estimation in Textured Scenes
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Photo tourism: exploring photo collections in 3D
ACM SIGGRAPH 2006 Papers
Estimating the spectral sensitivity of a digital sensor using calibration targets
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Priors for Large Photo Collections and What They Reveal about Cameras
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
What Do the Sun and the Sky Tell Us About the Camera?
International Journal of Computer Vision
Radiometric calibration from a single image
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Determining Surface Orientations of Specular Surfaces by Using the Photometric Stereo Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
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Photometric camera calibration is often required in physics-based computer vision. There have been a number of studies to estimate camera response functions (gamma function), and vignetting effect from images. However less attention has been paid to camera spectral sensitivities and white balance settings. This is unfortunate, since those two properties significantly affect image colors. Motivated by this, a method to estimate camera spectral sensitivities and white balance setting jointly from images with sky regions is introduced. The basic idea is to use the sky regions to infer the sky spectra. Given sky images as the input and assuming the sun direction with respect to the camera viewing direction can be extracted, the proposed method estimates the turbidity of the sky by fitting the image intensities to a sky model. Subsequently, it calculates the sky spectra from the estimated turbidity. Having the sky $$RGB$$RGB values and their corresponding spectra, the method estimates the camera spectral sensitivities together with the white balance setting. Precomputed basis functions of camera spectral sensitivities are used in the method for robust estimation. The whole method is novel and practical since, unlike existing methods, it uses sky images without additional hardware, assuming the geolocation of the captured sky is known. Experimental results using various real images show the effectiveness of the method.