3DOR 18

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https://diglib.eg.org/handle/10.2312/2632165

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Browsing 3DOR 18 by Subject "Computing methodologies"

Now showing 1 - 4 of 4
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    Automatic Extraction of Complex 3D Structures Application to the Inner Ear Segmentation from Cone Beam CT Digital Volumes
    (The Eurographics Association, 2018) Beguet, Florian; Mari, Jean-Luc; Cresson, Thierry; Schmittbuhl, Matthieu; Guise, Jacques A. de; Telea, Alex and Theoharis, Theoharis and Veltkamp, Remco
    We present an automatic approach for the retrieval of a complex structure within a 3D digital volume, using a generic deformable surface model. We apply this approach to the inner ear reconstruction of Cone Beam CT(CBCT) 3D data. The proposed method is based on a single prior shape initialization followed by two steps. A geometric rigid adjustment allows a close fit to inner ear boundaries. Finally, a Laplacian mesh deformation method is used to iteratively refine the mesh. Preliminary results are promising in terms of several similarity metrics.
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    Completion of Cultural Heritage Objects with Rotational Symmetry
    (The Eurographics Association, 2018) Sipiran, Ivan; Telea, Alex and Theoharis, Theoharis and Veltkamp, Remco
    Archaeological artifacts are an important part of our cultural heritage. They help us understand how our ancestors used to live. Unfortunately, many of these objects are badly damaged by the passage of time and need repair. If the object exhibits some form of symmetry, it is possible to complete the missing regions by replicating existing parts of the object. In this paper, we present a framework to complete 3D objects that exhibit rotational symmetry. Our approach combines a number of algorithms from the computer vision community that have had good performance at solving similar problems. In order to complete an archaeological artifact, we begin by scanning the object to produce a 3D mesh of triangles. We then preprocess the mesh to remove fissures and smoothen the surface. We continue by detecting the most salient vertices of the mesh (the key-points). If the object exhibits rotational symmetry, the key-points should form a circular structure which the Random Sample Consensus (RANSAC) algorithm should be able to detect. The axis of symmetry of the circle found should correspond to the axis of symmetry of the object. Thus, by rotating the mesh around the axis of the circle we should be able to complete a large portion of the missing regions. We alleviate any misalignment caused during the rotations via a non-rigid alignment procedure. In the evaluation, we compare the performance of our approach with other state of the art algorithms for 3D object completion. The benchmark proves that our algorithm is effective at completing damaged archaeological objects.
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    Performing Image-like Convolution on Triangular Meshes
    (The Eurographics Association, 2018) Tortorici, Claudio; Werghi, Naoufel; Berretti, Stefano; Telea, Alex and Theoharis, Theoharis and Veltkamp, Remco
    Image convolution with a filtering mask is at the base of several image analysis operations. This is motivated by Mathematical foundations and by the straightforward way the discrete convolution can be computed on a grid-like domain. Extending the convolution operation to the mesh manifold support is a challenging task due to the irregular structure of the mesh connections. In this paper, we propose a computational framework that allows convolutional operations on the mesh. This relies on the idea of ordering the facets of the mesh so that a shift-like operation can be derived. Experiments have been performed with several filter masks (Sobel, Gabor, etc.) showing state-of-the-art results in 3D relief patterns retrieval on the SHREC'17 dataset. We also provide evidence that the proposed framework can enable convolution and pooling-like operations as can be needed for extending Convolutional Neural Networks to 3D meshes.
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    Person Re-Identification from Depth Cameras using Skeleton and 3D Face Data
    (The Eurographics Association, 2018) Pala, Pietro; Seidenari, Lorenzo; Berretti, Stefano; Bimbo, Alberto Del; Telea, Alex and Theoharis, Theoharis and Veltkamp, Remco
    In the typical approach, person re-identification is performed using appearance in 2D still images or videos, thus invalidating any application in which a person may change dress across subsequent acquisitions. For example, this is a relevant scenario for home patient monitoring. Depth cameras enable person re-identification exploiting 3D information that captures biometric cues such as face and characteristic dimensions of the body. Unfortunately, face and skeleton quality is not always enough to grant a correct recognition from depth data. Both features are affected by the pose of the subject and the distance from the camera. In this paper, we propose a model to incorporate a robust skeleton representation with a highly discriminative face feature, weighting samples by their quality. Our method combining face and skeleton data improves rank-1 accuracy compared to individual cues especially on short realistic sequences.