We present a novel multi-view, projective texture mapping technique. While previous multi-view texturing approaches lead to blurring and ghosting artefacts if 3D geometry and/or camera calibration are imprecise, we propose a texturing algorithm that warps ("floats") projected textures during run-time to preserve crisp, detailed texture appearance. Our GPU implementation achieves interactive to real-time frame rates. The method is very generally applicable and can be used in combination with many image-based rendering methods or projective texturing applications. By using Floating Textures in conjunction with, e.g., visual hull rendering, light field rendering, or free-viewpoint video, improved rendering results are obtained from fewer input images, less accurately calibrated cameras, and coarser 3D geometry proxies.

Abstract. Videos acquired in low-light conditions often exhibit motion blur, which depends on the motion of the objects relative to the camera. This is not only visually unpleasing, but can hamper further processing. With this paper we are the first to show how the availability of stereo video can aid the challenging video deblurring task. We leverage 3D scene flow, which can be estimated robustly even under adverse conditions. We go beyond simply determining the object motion in two ways: First, we show how a piecewise rigid 3D scene flow representation allows to induce accurate blur kernels via local homographies. Second, we exploit the estimated motion boundaries of the 3D scene flow to mitigate ringing artifacts using an iterative weighting scheme. Being aware of 3D object motion, our approach can deal robustly with an arbitrary number of independently moving objects. We demonstrate its benefit over state-of-the-art video deblurring using quantitative and qualitative experiments on rendered scenes and real videos.

We present an image-based rendering system to viewpoint-navigate through space and time of complex real-world, dynamic scenes. Our approach accepts unsynchronized, uncalibrated multivideo footage as input. Inexpensive, consumer-grade camcorders suffice to acquire arbitrary scenes, for example in the outdoors, without elaborate recording setup procedures, allowing also for hand-held recordings. Instead of scene depth estimation, layer segmentation or 3D reconstruction, our approach is based on dense image correspondences, treating view interpolation uniformly in space and time: spatial viewpoint navigation, slow motion or freeze-and-rotate effects can all be created in the same way. Acquisition simplification, integration of moving cameras, generalization to difficult scenes and space-time symmetric interpolation amount to a widely applicable virtual video camera system.

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