A method performed by a head-mounted device for mapping common features between images that commonly represent an environment of a user wearing the head-mounted device using different light spectrum data, the method comprising: accessing a first image comprising image data representative of a first light spectrum ; accessing a second image comprising image data representative of a second light spectrum , wherein the first light spectrum is different to the second light spectrum ; feeding the first image and the second image as input to a deep neural network (DNN) ; causing the DNN to identify, within the first image and the second image, feature points that are common between the first image and the second image; generating a mapping that lists the feature points and that also lists coordinates of the feature points from both the first image and the second image, determining differences between the coordinates of the feature points in the first image and the coordinates of the feature points in the second image; warping the second image to cause the coordinates of the feature points in the second image to correspond to the coordinates of the feature points in the first image; and displaying a composite image comprising selected portions of the warped second image overlaid on top of corresponding portions of the first image, wherein the first image is a parallax corrected image, wherein a parallax corrected image comprises an image in which a difference in perspective between a camera of a head-mounted device and a pupil of a user of the head-mounted device is corrected and wherein a third image, which is a raw version of the first image prior to being subjected to a parallax correction, is also fed as input to the DNN, and wherein the DNN identifies changes made to the feature points in the first image during the parallax correction and applies related changes to the feature points in the second image. accessing a first image comprising image data representative of a first light spectrum ; accessing a second image comprising image data representative of a second light spectrum , wherein the first light spectrum is different to the second light spectrum ; feeding the first image and the second image as input to a deep neural network (DNN) ; causing the DNN to identify, within the first image and the second image, feature points that are common between the first image and the second image; generating a mapping that lists the feature points and that also lists coordinates of the feature points from both the first image and the second image, determining differences between the coordinates of the feature points in the first image and the coordinates of the feature points in the second image; warping the second image to cause the coordinates of the feature points in the second image to correspond to the coordinates of the feature points in the first image; and displaying a composite image comprising selected portions of the warped second image overlaid on top of corresponding portions of the first image, wherein the first image is a parallax corrected image, wherein a parallax corrected image comprises an image in which a difference in perspective between a camera of a head-mounted device and a pupil of a user of the head-mounted device is corrected and wherein a third image, which is a raw version of the first image prior to being subjected to a parallax correction, is also fed as input to the DNN, and wherein the DNN identifies changes made to the feature points in the first image during the parallax correction and applies related changes to the feature points in the second image. <EOS>