Let me preface this by saying:
1) Hi. I’m Kevin Falcetano and this is my first AISOS blog post. I am an undergraduate technician working for AISOS and have worked on the construction of our RTI equipment for almost two months.
2) This project was made far easier to complete because of Leszek Pawlowicz. His thorough documentation on the process of building an RTI dome and control system from consumer components as detailed on Hackaday was the reason for the successful and timely completion of AISOS’s very own RTI system. Another special thanks to the open software and materials from Cultural Heritage Imaging (CHI).
Okay, now that the introduction is out of the way, second things second.
What is RTI?
RTI stands for Reflectance Transformation Imaging. It is a method of digitizing/virtualizing the lighting characteristics of one face of an object by sampling multiple lighting angles from the same camera position over the object with known point light positions. The mathematical model involved produces a two dimensional image that can be relit from virtually any lighting angle, so that all of the surface detail is preserved on a per-pixel basis. The basic idea comes from the fact that if you have a surface, light reflects off of it differently and predictably depending on the angle of said surface. A visualization of surface normals, the vectors perpendicular to the surface at any given place, is provided below (credit CHI).
The information available is represented by yellow vectors, and the information we wish to calculate, the surface normals, is in red. So, given that we know the math behind how light can reflect from a surface (which we do), and we know the light path angle with respect to a fixed camera, we’re very close to calculating the normal vectors of the surface. We’re close because there are constants involved that are unknown, but if you’ve ever taken an algebra course, there is clearly a linear system that can be used to solve for those coefficients. We just need more data. That data comes in the form of images of lighting from more angles. This also helps to account for areas of an object where light from a certain angle may not hit due to occlusion by the object’s geometry, and is therefore incalculable. When all’s said and done, an RTI image is generated. Although it is two dimensional, an RTI image will be able to mimic the way the real object scatters light to a resolution that matches the source images. Below are three images as an example of how the lighting changes between each photo.
The openly available documentation for RTI explains its benefits better than I probably could, but put most simply, it’s like having an image, but the image knows how that object could be lit. This means that details that could not be revealed in any one possible image are shown in full in an RTI image. One of the pitfalls of photogrammetry, for instance, is it HATES reflective objects since smooth surfaces and specular/highlights make photogrammetry’s hallmark point tracking very difficult . RTI doesn’t care. RTI doesn’t need point overlap because the process asks that you eliminate many of the variables associated with photogrammetry, e.g. the camera and object do not move and the lighting angles are pre-calculated from a sphere of known geometry. The GIGAmacro can get up close, but it still produces images with single lighting angles, so, all else equal, less information per pixel. GIGAmacro, of course, has the advantage of being able to capture many camera positions very quickly, which results in many more pixels. RTI’s per-pixel information produces near perfect normal maps of the surface, which is represented with a false color standard. As an added benefit, our automated version of the workflow is blazing fast. Like, from start to RTI image file takes as little as five minutes, fast.
For a simple example of how RTI lets you relight an object, take a look at this coin. Click the lightbulb icon and then click and drag your mouse to move the lighting. This same data can be processed in a multitude of ways to reveal other details.
There are many ways to record data for RTI, including some very manually intensive methods that, for the sake of expedience, are definitely out of the question for AISOS. We operate under the assumption that researchers who use our space may have limited experience in any of their desired techniques, so the easier we can make powerful data acquisition and analysis methods the better. We decided to use Leszek’s method because it was both cost and time effective due to the way it can easily be automated. This method involves putting as many white LEDs as we would like data points, up to 64, inside an acrylic dome with a hole in the very top. The hole is there to allow a camera to look vertically down through it at an object centered under the dome. The dome is painted black on the inside to ensure internal reflections are minimized, making the only lighting source for the object the desired single LED inside the dome. Every LED is lit once for each image, and the data points may be processed and turned into an RTI image file.
This way, every LED can be turned on individually to take a picture of each lighting angle without moving or changing anything, and done so with an automatic shutter. This means that after setup, the image capture process is completely automated. Ultimately, the goal for this build was: Place dome over object, move in camera on our boom arm, focus lens, press button, RTI happens. Observe this accomplished goal in picture form:
You may notice in the above image that there are two domes of different sizes. We built two not only to accommodate different sizes of objects, but also to be able to use certain lenses, normally macro ones, that need to be closer to an object to be in focus. This allows us to pick a preferred lens for an object and then decide which dome to use with it so that the desired magnification and lens distance can be retained in most situations.
How all of this was built is outlined in a separate blog post.