CEIG19

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https://diglib.eg.org/handle/10.2312/2632793

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Browsing CEIG19 by Subject "Computer"

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    Perfect Spatial Hashing for Point-cloud-to-mesh Registration
    (The Eurographics Association, 2019) Mejia-Parra, Daniel; Lalinde-Pulido, Juan; Sánchez, Jairo R.; Ruiz-Salguero, Oscar; Posada, Jorge; Casas, Dan and Jarabo, Adrián
    Point-cloud-to-mesh registration estimates a rigid transformation that minimizes the distance between a point sample of a surface and a reference mesh of such a surface, both lying in different coordinate systems. Point-cloud-to-mesh-registration is an ubiquitous problem in medical imaging, CAD CAM CAE, reverse engineering, virtual reality and many other disciplines. Common registration methods include Iterative Closest Point (ICP), RANdom SAmple Consensus (RANSAC) and Normal Distribution Transform (NDT). These methods require to repeatedly estimate the distance between a point cloud and a mesh, which becomes computationally expensive as the point set sizes increase. To overcome this problem, this article presents the implementation of a Perfect Spatial Hashing for point-cloud-to-mesh registration. The complexity of the registration algorithm using Perfect Spatial Hashing is O(NYxn) (NY : point cloud size, n: number of max. ICP iterations), compared to standard octrees and kd-trees (time complexity O(NY log(NT)xn), NT : reference mesh size). The cost of pre-processing is O(NT +(N3H )2) (N3H : Hash table size). The test results show convergence of the algorithm (error below 7e-05) for massive point clouds / reference meshes (NY = 50k and NT = 28055k, respectively). Future work includes GPU implementation of the algorithm for fast registration of massive point clouds.
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    Sensitivity Analysis in Shape Optimization using Voxel Density Penalization
    (The Eurographics Association, 2019) Montoya-Zapata, Diego; Acosta, Diego A.; Moreno, Aitor; Posada, Jorge; Ruiz-Salguero, Oscar; Casas, Dan and Jarabo, Adrián
    Shape optimization in the context of technical design is the process by which mechanical demands (e.g. loads, stresses) govern a sequence of piece instances, which satisfy the demands, while at the same time evolving towards more attractive geometric features (e.g. lighter, cheaper, etc.). The SIMP (Solid Isotropic Material with Penalization) strategy seeks a redistribution of local densities of a part in order to stand stress / strain demands. Neighborhoods (e.g. voxels) whose density drifts to lower values are considered superfluous and removed, leading to an optimization of the part shape. This manuscript presents a study on how the parameters governing the voxel pruning affect the convergence speed and performance of the attained shape. A stronger penalization factor establishes the criteria by which thin voxels are considered void. In addition, the filter discourages punctured, chessboard pattern regions. The SIMP algorithm produces a forecasted density map on the whole piece voxels. A post-processing is applied to effectively eliminate voxels with low density, to obtain the effective shape. In the literature, mechanical performance finite element analyses are conducted on the full voxel set with diluted densities by linearly weakening each voxel resistance according to its diluted density. Numerical tests show that this approach predicts a more favorable mechanical performance as compared with the one obtained with the shape which actually lacks the voxels with low density. This voxel density - based optimization is particularly convenient for additive manufacturing, as shown with the piece actually produced in this work. Future endeavors include different evolution processes, albeit based on variable density voxel sets, and mechanical tests conducted on the actual sample produced by additive manufacture.
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    User-reconfigurable CAD Feature Recognition in 1- and 2-topologies with Reduction of Search Space via Geometry Filters
    (The Eurographics Association, 2019) Corcho, Juan Camilo Pareja; Acosta, Oscar Mauricio Betancur; Ruiz, Oscar E.; Cadavid, Carlos; Casas, Dan and Jarabo, Adrián
    In the context of Computer-Aided Design and Manufacturing, the problem of feature recognition plays a key role in the integration of systems. Until now, compromises have been reached by only using FACE-based geometric information of prismatic CAD models to prune the search domain. This manuscripts presents a feature recognition method which more aggressively prunes the search space with reconfigurable geometric tests. This reconfigurable approach allows to enforce arbitrary confluent tests which are topologic and geometric, with enlarged domain. The test sequence is itself a graph (i.e. not a linear or total-order sequence). Unlike the existing methods which are FACE-based, the present one permits combinations of topologies whose dimensions are 2, 1 or 0. This system has been implemented in an industrial environment. The industrial incarnation allows industry-based customization and is faster when compared to topology-based feature recognition. Future work is required in improving robustness of search conditions and improving the graphic input interface.