15256053
Optimized triangle mesh reconstruction from unstructured points
A variety of approaches have been proposed for polygon mesh reconstruction from a set of unstructured sample points. Suffering from severe aliases at sharp features and having a large number of unnecessary faces, most resulting meshes need to be optimized using input sample points in a postprocess. In this paper, we propose a fast algorithm to reconstruct high-quality meshes from sample data. The core of our proposed algorithm is a new mesh evaluation criterion which takes full advantage of the relation between the sample points and the reconstructed mesh. Based on our proposed evaluation criterion, we develop necessary operations to efficiently incorporate the functions of data preprocessing, isosurface polygonization, mesh optimization and mesh simplification into one simple algorithm, which can generate high-quality meshes from unstructured point clouds with time and space efficiency.
11221345
Multiresolution free form object modeling with point sampled geometry
In this paper an efficient framework for the creation of 3D digital content with point sampled geometry is proposed. A new hierarchy of shape representations with three levels is adopted in this framework. Based on this new hierarchical shape representation, the proposed framework offers concise integration of various volumetric- and surface-based modeling techniques, such as Boolean operation, offset, blending, free-form deformation, parameterization and texture mapping, and thus simplifies the complete modeling process. Previously to achieve the same goal, several separated algorithms had to be used independently with inconsistent volumetric and surface representations of the free-form object. Both graphics and industrial applications are presented to demonstrate the effectiveness and efficiency of the proposed framework.
5878659
An optimization algorithm for free-form surface partitioning based on weighted gaussian image
Partitioning free-form surfaces into sub-patches and finding optimal representative normal for each patch to maximize a global objective function is an important two-level operation in diverse industrial applications. In this paper, by solving a maximum hemispherical partitioning problem raised from a weighted Gaussian image, an optimization algorithm is proposed to partition a free-form surface into two sub-patches and simultaneously report the optimal representative normals. By discretizing the free-form surface with W sample points and clustering normals on the surface with m distinct sample normals, the proposed algorithm is designed, in general, with O(m2W2) time complexity and O(W2) space complexity, and in particular, if the surface is convex, in O(m2 logm) time complexity. Case studies with four representative examples are presented and a real world application is exploited to demonstrate the effectiveness and usefulness of the proposed algorithm.
2900427
Reconstructing B-spline Curves from Point Clouds--A Tangential Flow Approach Using Least Squares Minimization
We present a novel algorithm based on least-squares minimization to approximate point cloud data in 2D plane with a smooth B-spline curve. The point cloud data may represent an open curve with self intersection and sharp corner. Unlike other existing methods, such as the moving least-squares method and the principle curve method, our algorithm does not need a thinning process. The idea of our algorithm is intuitive and simple - we make a B-spline curve grow along the tangential directions at its two end-points following local geometry of point clouds. Our algorithm generates appropriate control points of the fitting B-spline curve in the least squares sense. Although presented for the 2D case, our method can be extended in a straightforward manner to fitting data points by a B-spline curve in higher dimensions
18353926
Inverse validation for accurate range image registration with structured data
Automatic range image registration is a fundamental yet extremely difficult problem in machine vision. In this paper, we extend a promising registration algorithm, the geometric iterative closest point (GICP), for structured image data. The extended algorithm is based on a relaxation method combining the motion estimation results from the possible correspondences established by the GICP algorithm and those that are further evaluated by an inverse validation procedure. A comparative study based on real images has shown that the extended algorithm is more accurate and robust than the original algorithm.
3748150
Mesh blending
A new method for smoothly connecting different patches on triangle meshes with arbitrary connectivity, called mesh blending, is presented. A major feature of mesh blending is to move vertices of the blending region to a virtual blending surface by choosing an appropriate parameterization of those vertices. Once blending is completed, the parameterization optimization is performed to perfect the final meshes. Combining mesh blending with multiresolution techniques, an effective blending technique for meshes is obtained. Our method has several advantages: (1) the user can intuitively control the blending result using different blending radii, (2) the shape of cross-section curves can be adjusted to flexibly design complex models, and (3) the resulting mesh has the same connectivity as the original mesh. In this paper, some examples about smoothing, sharpening, and mesh editing show the efficiency of the method.
772940
Fitting subdivision surfaces to unorganized point data using SDM
We study the reconstruction of smooth surfaces from point clouds. We use a new squared distance error term in optimization to fit a subdivision surface to a set of unorganized points, which defines a closed target surface of arbitrary topology. The resulting method is based on the framework of squared distance minimization (SDM) proposed by Pottmann et al. Specifically, with an initial subdivision surface having a coarse control mesh as input, we adjust the control points by optimizing an objective function through iterative minimization of a quadratic approximant of the squared distance function of the target shape. Our experiments show that the new method (SDM) converges much faster than the commonly used optimization method using the point distance error function, which is known to have only linear convergence. This observation is further supported by our recent result that SDM can be derived from the Newton method with necessary modifications to make the Hessian positive definite and the fact that the Newton method has quadratic convergence.
41398857
BIRD: Biomedical Information Integration and Discovery with Semantic Web Services
Biomedical research is now information intensive; the volume and diversity of new data sources challenges current database technologies. The development and tuning of database technologies for biology and medicine will maintain and accelerate the current pace for innovation and discovery. New promising application fields such as the Semantic Web and Semantic Web Services can leverage the potential of biomedical information integration and discovery, facing the problem of semantic heterogeneity of biomedical information sources in a variety of storage and data formats widely distributed both across the Internet and within individual organizations. In this paper, we present BIRD, a fully-fledged biomedical information integration solution that combines natural language analysis and semantically-empowered techniques to ascertain how the user needs can be best fit. Our approach is backed with a proof-of-concept implementation where the breakthrough and efficiency of integrating the biomedical publications database PubMed, the Database of Interacting Proteins (DIP) and the Munich Information Center for Protein Sequences (MIPS) has been tested.