Goal: I am working on accurately calibrating a camera using a single image of glitter.
Paper Title: SparkleCalibration: Glitter Imaging Model for Single-Image Calibration ... maybe
Venue: CVPR 2020
For the last week, I have been specifically working on getting the calibration pipeline and error function up and running using the receptive fields of the glitter. Now, in the error function, I am predicting the intensity of each piece of glitter, and comparing this predicted intensity to the actual intensity (measured from the image we capture from the camera). I am also using a single receptive field for all of the glitter pieces instead of different receptive fields for each glitter piece, because we found that there were enough 'bad' receptive fields to throw off the normalization of the predicted intensities in the optimization.
This plot shows the predicted vs. measured intensities of all of the glitter pieces we "see" in our image (many have very low intensities since most glitter pieces we "see" are not actually lit. Here we see that there is a slightly noticeable trend along the diagonal as we expect to see. The red points are the glitter pieces which are incorrectly predicted to be off, the green points are the glitter pieces which are correctly predicted to be on, the black points are the glitter pieces which are incorrectly predicted to be on, and the rest are all of the other glitter pieces.
I also tried using the old on/off method for the error function (distance from the light location as the error function) and found that the results were quite a bit worse than the receptive field method (yay!)
Goal: My next tasks are the following:
- search for the right gaussian to use for all the glitter pieces as the receptive field
- run the checkerboard calibration for my currently physical setup
- re-take all images in base and shifted orientations, re-measure the physical setup, take 'white-square' test images in this setup, and maybe some iphone pictures