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Projection-based displays are often used to create large, immersive environments for virtual reality (VR), simulation, and training. These displays have become increasingly widespread due to the decreasing cost of projectors and advances in calibration and rendering that allow the images of multiple projectors to be registered together accurately on complex display surfaces, while simultaneously compensating for the surface shape--a process that requires knowledge of the pose of each projector as well as a geometric representation of the display surface. A common limitation of many techniques used to calibrate multi-projector displays is that they cannot be applied continuously, as the display is being used, to ensure projected imagery remains precisely registered on a potentially complex display surface. This lack of any continuous correction or refinement means that if a projector is moved slightly out of alignment, either suddenly or slowly over time, use of the display must be interrupted, possibly for many minutes at a time, while system components are recalibrated. This dissertation proposes a novel framework for continuous calibration where intelligent projector units (projectors augmented with cameras and dedicated computers) interact cooperatively as peers to continuously estimate display parameters during system operation. Using a Kalman filter to fuse local camera image measurements with remote measurements obtained by other projector units, the projector units in a multi-projector display cooperatively estimate the poses of all projectors and information about the display surface, such as its pose or shape, in a continuous fashion. This decentralized approach to continuous calibration has the potential to enable scalable and fault-tolerant display solutions that can be configured and maintained by untrained users.