CEIG19

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

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Browsing CEIG19 by Subject "based models"

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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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    View-dependent Hierarchical Rendering of Massive Point Clouds through Textured Splats
    (The Eurographics Association, 2019) Comino Trinidad, Marc; Calaf, Antonio Chica; Gran, Carlos Andújar; Casas, Dan and Jarabo, Adrián
    Nowadays, there are multiple available range scanning technologies which can capture extremely detailed models of realworld surfaces. The result of such process is usually a set of point clouds which can contain billions of points. While these point clouds can be used and processed offline for a variety of purposes (such as surface reconstruction and offline rendering) it is unfeasible to interactively visualize the raw point data. The most common approach is to use a hierarchical representation to render varying-size oriented splats, but this method also has its limitations as usually a single color is encoded for each point sample. Some authors have proposed the use of color-textured splats, but these either have been designed for offline rendering or do not address the efficient encoding of image datasets into textures. In this work, we propose extending point clouds by encoding their color information into textures and using a pruning and scaling rendering algorithm to achieve interactive rendering. Our approach can be combined with hierarchical point-based representations to allow for real-time rendering of massive point clouds in commodity hardware.