Scene flow is the three-dimensional motion field of points in the world, just as optical flow is the twodimensional motion field of points in an image. Any optical flow is simply the projection of the scene flow onto the image plane of a camera. In this paper, we present a framework for the computation of dense, non-rigid scene flow from optical flow. Our approach leads to straightforward linear algorithms and a classification of the task into three major scenarios: (1) complete instantaneous knowledge of the scene structure, (2) knowledge only of correspondence information, and (3) no knowledge of the scene structure. We also show that multiple estimates of the normal flow cannot be used to estimate dense scene flow directly without some form of smoothing or regularization.
Digital photographs and video are exciting inventions that let us capture the visual experience of events around us in a computer and re-live the experience, although in a restrictive manner. Photographs only capture snapshots of a dynamic event, and while video does capture motion, it is recorded from pre-determined positions and consists of images discretely sampled in time, so the timing cannot be changed.This thesis presents an approach for re-rendering a dynamic event from an arbitrary viewpoint with any timing, using images captured from multiple video cameras. The event is modeled as a non-rigidly varying dynamic scene captured by many images from different viewpoints, at discretely sampled times. First, the spatio-temporal geometric properties (shape and instantaneous motion) are computed. Scene flow is introduced as a measure of non-rigid motion and algorithms to compute it, with the scene shape. The novel view synthesis problem is posed as one of recovering corresponding points in the original images, using the shape and scene flow. A reverse mapping algorithm, ray-casting across space and time, is de-
In this paper, we present an "appearance-based" virtual view generation method for temporally-varying events taken by multiple cameras of the "3D Room", developed by our group. With this method, we can generate images from any virtual view point between two selected real views. The virtual appearance view generation method is based on simple interpolation between two selected views. The correspondence between the views are automatically generated from the multiple images by use of the volumetric model shape reconstruction framework. Since the correspondences are obtained by the recovered volumetric model, even occluded regions in the views can be correctly interpolated in the virtual view images. The virtual view image sequences are presented for demonstrating the performance of the virtual view image generation in the 3D Room. 9 cameras on the ceiling 10 cameras on each wall (40 in total)
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