37-Issue 2

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https://diglib.eg.org/handle/10.2312/2632120

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Browsing 37-Issue 2 by Subject "based models"

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    PCPNet: Learning Local Shape Properties from Raw Point Clouds
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Guerrero, Paul; Kleiman, Yanir; Ovsjanikov, Maks; Mitra, Niloy J.; Gutierrez, Diego and Sheffer, Alla
    In this paper, we propose PCPNET, a deep-learning based approach for estimating local 3D shape properties in point clouds. In contrast to the majority of prior techniques that concentrate on global or mid-level attributes, e.g., for shape classification or semantic labeling, we suggest a patch-based learning method, in which a series of local patches at multiple scales around each point is encoded in a structured manner. Our approach is especially well-adapted for estimating local shape properties such as normals (both unoriented and oriented) and curvature from raw point clouds in the presence of strong noise and multi-scale features. Our main contributions include both a novel multi-scale variant of the recently proposed PointNet architecture with emphasis on local shape information, and a series of novel applications in which we demonstrate how learning from training data arising from well-structured triangle meshes, and applying the trained model to noisy point clouds can produce superior results compared to specialized state-of-the-art techniques. Finally, we demonstrate the utility of our approach in the context of shape reconstruction, by showing how it can be used to extract normal orientation information from point clouds.
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    PointProNets: Consolidation of Point Clouds with Convolutional Neural Networks
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Roveri, Riccardo; Ă–ztireli, A. Cengiz; Pandele, Ioana; Gross, Markus; Gutierrez, Diego and Sheffer, Alla
    With the widespread use of 3D acquisition devices, there is an increasing need of consolidating captured noisy and sparse point cloud data for accurate representation of the underlying structures. There are numerous algorithms that rely on a variety of assumptions such as local smoothness to tackle this ill-posed problem. However, such priors lead to loss of important features and geometric detail. Instead, we propose a novel data-driven approach for point cloud consolidation via a convolutional neural network based technique. Our method takes a sparse and noisy point cloud as input, and produces a dense point cloud accurately representing the underlying surface by resolving ambiguities in geometry. The resulting point set can then be used to reconstruct accurate manifold surfaces and estimate surface properties. To achieve this, we propose a generative neural network architecture that can input and output point clouds, unlocking a powerful set of tools from the deep learning literature. We use this architecture to apply convolutional neural networks to local patches of geometry for high quality and efficient point cloud consolidation. This results in significantly more accurate surfaces, as we illustrate with a diversity of examples and comparisons to the state-of-the-art.