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OpenCV in C++ can be used to identify variations in lighting and perspective in two related images by using feature detection and matching techniques.

1. Feature Detection: Using feature detection algorithms (such as SIFT, SURF, ORB) we can identify keypoints and descriptors in both images.

2. Feature Matching: We can match the features between the two images using feature matching algorithms. There are several algorithms that can be used for this purpose (such as FLANN, Brute-Force, etc.).

3. Homography Estimation: Using the matched features, we can estimate the homography matrix using OpenCV’s findHomography function. The homography matrix represents the mapping between the two images and can be used to align the images.

4. Perspective Transformation: Using the homography matrix, we can perform a perspective transformation to align the images. This will correct for any perspective distortions between the two images.

5. Lighting variations correction: To correct for lighting variations, we can use algorithms such as Histogram Equalization to equalize the histograms of both images. This will help to balance the lighting in both images.

Once the images are aligned and any lighting variations are corrected, we can perform further analysis on them using openCV. For example, we can compare pixel values in corresponding regions of the two images to identify differences.