PROCAMS '08 Proceedings of the 5th ACM/IEEE International Workshop on Projector camera systems
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Multi-projector systems provide scalability for low-cost, high-resolution display needed for many applications including data visualization, telepresence, and ambient or immersive displays. But geometric calibration of the systems is a great challenge, and for many display configurations, existing camera-based techniques are unsatisfactory. A simple, non-parametric robust technique exists for single-camera calibration, but it doesn't scale to high-resolution displays. Scalability can be gained with a parametric model, but general approaches have tended to compromise robustness or accuracy. Special-purpose models have improved the situation, but at the cost of flexibility. We describe a hybrid calibration framework that combines the robustness of non-parametric calibration with the scalability of parametric calibration. Each parametric calibration technique has a hybrid cousin. The parametric model is fit just as before, but it is not directly used by the rendering algorithm. Instead, the rendering algorithm resembles a non-parametric renderer–it corrects the rendered output using an interpolated lookup table of correspondences between projector framebuffers and a common space. But the space isn't the camera's image; rather it consists of the screen points reconstructed according to the parametric model. By structuring the correspondences and lookup table in a particular way, we are able to ensure that parametric model-fitting error does not hurt the calibration's registration accuracy. We achieve accurate calibration with a general parametric model. Projectors are not modelled, and their radial distortion is corrected implicitly. Multiple cameras are supported. The calibration supports headtracked immersive rendering. A number of related and ancillary research contributions are described in this dissertation, including an analysis of resolution loss in two-pass rendering, together with a technique for preventing the loss by dividing the display surface into clusters based on surface normal. Also techniques are described which improve the reliability of the structure from motion estimation needed for calibration. Other contributions include work in precision feature localization and uncertainty estimation, and a technique for calibrating radial distortion using any number of cameras and without knowledge or assumptions about scene structure. We believe that this work provides key contributions towards robust, hassle-free strong geometric calibration of wide-area, heterogeneous multi-projector environments. Keywords: Projector-Camera Systems, Camera Calibration, Feature Matching, Multi-View Geometry, 3D Reconstruction .