Many aspects of multimedia are restricted by a lack of automation or hardware constraints, such as very low bit-rate communications, and it is in these areas that computer vision can provide novel solutions. Multimedia is a relatively new and rapidly expanding area of computer science that involves the generation, visualisation/realisation and communication of all types of digital information. We provide a tight error bound on the resulting surface and report experimental results on a variety of CAD models.Īs the subject of computer vision develops, the possibilities of its application to solving problems in other disciplines of computer science becomes apparent. Then the offset boundary is generated as the isosurface using these voxels and the associated offset points. The offset points and normals are sufficiently dense to ensure that all voxels between the original and the offset surfaces are properly labeled as either too close to the original solid or possibly containing the offset surface. Each face, edge, and vertex of the original solid generate a set of offset points spaced along the (pencil of) normals associated with it. Our approach is based on a hybrid data structure combining point samples, voxels, and continuous surfaces. We introduce a new fast, and very simple method for offsetting (growing and shrinking) a solid model by arbitrary distance r. Offsetting is important for stereolithography, NC machining, rounding corners, collision avoidance, and Hausdorff error calculation. We address the delicate problem of offsetting polygonal meshes. Keywords: Computer vision, 3-D model construction, image sequence (motion) analysis, optic flow, Kalman filter, surface interpolation, computer aided design, computer graphics animation. We demonstrate the application of our new techniques to several real image sequences. These points are then used to construct a finite element surface model, which is itself refined over time. Nearby points from successive frames are merged to improve the position estimates. It then projects individual measurements into 3-D points with associated uncertainties. The algorithm starts with a flow field computed using local correlation.
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In this paper, we extend this approach to more general motion and use a full 3-D surface model instead of a 2 1 = 2 -D sketch. Previous research in depth from motion has demonstrated the power of using an incremental approach to depth estimation. This paper examines the construction of a 3-D surface model of an object rotating in front of a camera. Experiments with real world imagery demonstrate the validity of the approach. Moreover, shape adjustments can be constrained such that the recovered model's silhouette matches those of the input images.
The texture space formulation has improved computational complexity over standard image-based error aproaches, and allows computation of the reprojection error and uncertainty for any point on the surface.
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The surface is deformed towards a photometrically-consistent solution via a series of 1D epipolar searches at randomly selected surface points. Next, the surface shape is adjusted to minimize residual error in texture space. The texture map and its associated residual error image are obtained via maximum a posteriori estimation and reprojection of the multiple views into texture space. In each iteration, the method first estimates a texture map given the current shape estimate. An iterative method for reconstructing a 3D polygonal mesh and color texture map from multiple views of an object is presented.