3DOR 13

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https://diglib.eg.org/handle/10.2312/308

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Browsing 3DOR 13 by Subject "Applications"

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    Compact Vectors of Locally Aggregated Tensors for 3D Shape Retrieval
    (The Eurographics Association, 2013) Tabia, Hedi; Picard, David; Laga, Hamid; Gosselin, Philippe-Henri; Umberto Castellani and Tobias Schreck and Silvia Biasotti and Ioannis Pratikakis and Afzal Godil and Remco Veltkamp
    During the last decade, a significant attention has been paid, by the computer vision and the computer graphics communities, to three dimensional (3D) object retrieval. Shape retrieval methods can be divided into three main steps: the shape descriptors extraction, the shape signatures and their associated similarity measures, and the machine learning relevance functions. While the first and the last points have vastly been addressed in recent years, in this paper, we focus on the second point; presenting a new 3D object retrieval method using a new coding/pooling technique and powerful 3D shape descriptors extracted from 2D views. For a given 3D shape, the approach extracts a very large and dense set of local descriptors. From these descriptors, we build a new shape signature by aggregating tensor products of visual descriptors. The similarity between 3D models can then be efficiently computed with a simple dot product. We further improve the compactness and discrimination power of the descriptor using local Principal Component Analysis on each cluster of descriptors. Experiments on the SHREC 2012 and the McGill benchmarks show that our approach outperforms the state-of-the-art techniques, including other BoF methods, both in compactness of the representation and in the retrieval performance.
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    Geometric Histograms of 3D Keypoints for Face Identification with Missing Parts
    (The Eurographics Association, 2013) Berretti, Stefano; Werghi, Naoufel; Bimbo, Alberto del; Pala, Pietro; Umberto Castellani and Tobias Schreck and Silvia Biasotti and Ioannis Pratikakis and Afzal Godil and Remco Veltkamp
    In this work, an original solution to 3D face identification is proposed, which supports recognition also in the case of probes with missing parts. Distinguishing traits of the face are captured by first extracting 3D keypoints of a face scan, then measuring how the face surface changes in the keypoints neighborhood using a local descriptor. To this end, an adaptation of the meshDOG algorithm to the case of 3D faces is proposed, together with a multi-ring geometric histogram descriptor. Face similarity is then evaluated by comparing local keypoint descriptors across inlier pairs of matching keypoints between probe and gallery scans. Experiments have been performed to assess the keypoints distribution and repeatability. Recognition accuracy of the proposed approach has been evaluated on the Bosphorus database, showing competitive results with respect to existing 3D face biometrics solutions.
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    SymPan: 3D Model Pose Normalization via Panoramic Views and Reflective Symmetry
    (The Eurographics Association, 2013) Sfikas, Konstantinos; Pratikakis, Ioannis; Theoharis, Theoharis; Umberto Castellani and Tobias Schreck and Silvia Biasotti and Ioannis Pratikakis and Afzal Godil and Remco Veltkamp
    A novel pose normalization method, based on panoramic views and reflective symmetry, is presented. Initially, the surface of a 3D model is projected onto the lateral surface of a circumscribed cylinder, aligned with the primary principal axis of space. Based on this cylindrical projection, a normals' deviation map is extracted and using an octree-based search strategy, the rotation which optimally aligns the primary principal axis of the 3D model and the cylinder's axis is computed. The 3D model's secondary principal axis is then aligned with the secondary principal axis of space in a similar manner. The proposed method is incorporated in a hybrid scheme, that serves as the pose normalization method in a state-of-the-art 3D model retrieval system. The effectiveness of this system, using the hybrid pose normalization scheme, is evaluated in terms of retrieval accuracy and the results clearly show improved performance against current approaches.