If the geometry of the two cameras is known, you can solve this problem relatively easily.
You have your 3D scanner set up at a known location, producing triplets of points $x_s,y_s,z_s$. These points could be anything, from converted $x,y,z$ to rotation, elevation, distance. But, at the end of the day, each one of these points bears information about a block of space infront of the 3d scanner.
The same block of space is now also viewed by a typical camera, set up at a known (different) location which only produces $x_c,y_c, brightness$ points.
So, the point now is to transform the $x_s,y_s,z_s$ so that they match their $x_c,y_c$. This is relatively easy, if you already know the camera's transformation matrix that takes points from the world space (your scanner's space) to image space (your camera's output).
For more information, please see this link, this link, this link, this link or more generally look for anything related to "camera calibration".
Hope this helps.
I am using the term "Geometry" here to denote that at the very least, the two cameras should be imaging the same space and at the very least, you should know the orientation of one with respect to the other and their relative positions.
If you don't know any of this information, you could still try to co-register the two but with considerably much more effort.
The very least your 3D scanner should support in this case would be to return actual colour for every point in 3D space.
The basic idea would be to calibrate the optical camera to recover its transformation matrix. So, now you have a way of transforming 3D point clouds to 2D images that look almost the same as if they were shot by the optical camera.
But here comes the difficult part: Because you don't know the two cameras relative positions and orientations, you would have to try a huge amount of combinations to find the one that would match your original camera configuration.
Furthermore, there will be combinations of locations / orientations that cannot be investigated because of shadowing. So, co-registration only makes sense if the optical camera's position and orientation is WITHIN the spherical space that the 3D scanner is imaging. Even then, there might be limits to how many equivalent 2D views you could produce depending on the geometry of the objects being imaged.
Happy to ammend this response with more specific information if more details are added to the question. What would be really useful would be and extract of the actual data.
Hope this helps.