A Projector-Camera System with Real-Time Photometric Adaptation for Dynamic Environments

Abstract

Camera Assisted ProjectionThe recent availability of cheap, small, and bright projectors has made it practical to use them for a wide range of applications such as creating large seamless displays [8,11,12,25] and immersive environments [5,21]. By introducing a camera into the system, and applying techniques from computer vision, the projection system can operate taking its environment into account. For example, it is possible to allow users to interact with the projected image creating projected interfaces [1,10,24]. The camera can assist projection by taking into account distortions due to surface geometry [18,19], or eliminate shadows cast on the projected image [9,22]. The camera can also assist projection by color correcting a homogeneous colored surface [6], or by correcting for spatially varying color and texture [14,3]. Related methods have been applied to improve the recovery of 3D geometry from structured light [4], to neutralize the appearance of a painting [2], to restore color-damaged paintings [26], and to control the appearance of 3D objects [7].All previous work that takes into account both geometric and photometric properties of projection has assumed both a static scene and projection system. The assumption that the scene and system remain static is very restrictive. This is especially true when we consider recently presented applications that require hand-held or mobile projection systems such as iLamps and RFIG Lamps [18,20]. Even when the projector is not mobile, there are systems that use computer-controlled motors to steer the projected image onto any surface in an environment [13,16]. For applications in which the projector is fixed, the scene may be dynamic, such as with illuminating clay (a 3D tangible interface) [17]. It is thus critical for projectorcamera systems to be able to take into account both geometric and photometric changes.One approach that was proposed to handle photometric changes is direct dynamic feedback from the camera for each pixel [14]. This method requires several frames to converge, limiting its use to projecting static images on static scenes. A method for projection of video on static scenes was proposed in [7,14] using a photometric model. The parameters of this model are determined off-line by projecting and capturing a sequence of calibration images. Arbitrary images (video) are then compensated on-line, before projection, using the same model; the model is valid as long as the scene remains static. Whenever the scene changes, however, the calibration images must be re-projected making this method impractical in a dynamic environment.In this work, we present a novel hybrid method which combines a model based approach with dynamic feedback

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