We have a setup that combines ToF, structured light and multiple colour cameras to reconstruct hands from the elbow down. Short version: it's a massive pain in the ass. In fact the setup really only works because we have a preconceived motion model (particular hand gestures) and have carefully arranged the scene to avoid interference. I'm unaware of a general solution where you can just throw more cameras in and get better scene data.
One neat thing though you might want to look at: if all you have is structured light (ie Kinect v1) you can simply attach a vibrating motor to each emitter/receiver to avoid a lot of interference per [0]
[0] https://wwwx.cs.unc.edu/~maimone/media/kinect_VR_2012.pdf
Can anybody point me to some literature or reference materials about attempts to combine the inputs from multiple techniques simultaneously?
E.g. a device with stereo conventional cameras and infrared cameras & emitters which compares the resulting model from each input source/technique and actively re-adjusts final depth estimate?
Is "sensor fusion" the right jargon to use in this context?
Or, even crazier, a control system which actively jitters the camera's pose to gain more information for points in the depth map with lower confidence scores / conflicting estimates?
But maybe such a setup is overly complex and yields minimal gains in mixed indoor & outdoor scenarios?