3DOR 14

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https://diglib.eg.org/handle/10.2312/7710

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Browsing 3DOR 14 by Subject "Applications"

Now showing 1 - 4 of 4
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    Automatic Location of Landmarks used in Manual Anthropometry
    (The Eurographics Association, 2014) Giachetti, A.; Mazzi, E.; Piscitelli, F.; Aono, M.; Hamza, A. Ben; Bonis, T.; Claes, P.; Godil, A.; Li, C.; Ovsjanikov, M.; Patraucean, V.; Shu, C.; Snyders, J.; Suetens, P.; Tatsuma, A.; Vandermeulen, D.; Wuhrer, S.; Xi, P.; Benjamin Bustos and Hedi Tabia and Jean-Philippe Vandeborre and Remco Veltkamp
    In this paper we report the results of the SHREC 2014 track on automatic location of landmarks used in manual anthropometry. The track has been organized to test the ability of modern computational geometry/pattern recognition techniques to locate accurately reference points used for tape based measurement. Participants had to locate six specific landmarks on human models acquired with a structured light body scanner. A training set of 50 models with manual annotations of the corresponding landmarks location was provided to train the algorithms. A test set of 50 different models was also provided, without annotations. Accuracy of the automatic location methods was tested via computing geodesic distances of the detected points from manually placed ones and evaluating different quality scores and functions.
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    Fisher Encoding of Adaptive Fast Persistent Feature Histograms for Partial Retrieval of 3D Pottery Objects
    (The Eurographics Association, 2014) Savelonas, Michalis A.; Pratikakis, Ioannis; Sfikas, Konstantinos; Benjamin Bustos and Hedi Tabia and Jean-Philippe Vandeborre and Remco Veltkamp
    Cultural heritage is a natural application domain for partial 3D object retrieval, since it usually involves objects that have only been partially preserved. This work introduces a method for the retrieval of 3D pottery objects, based on partial point cloud queries. The proposed method extracts fast persistent feature histograms calculated adaptively to the mean point distances of the point cloud query. The extracted set of vectors is refined by a denoising component, which employs statistical filtering. The remaining vectors are further refined by a filtering component, which discards points surrounded by surfaces of extremely fine-grained irregularity, often associated with artefact damages. A bag of visual words scheme is used, which starts from the final set of persistent feature histogram vectors and estimates Gaussian mixture models by means of an expectation maximization algorithm. The resulting Gaussian mixture models define the visual codebook, which is used within the context of Fisher encoding. Experiments are performed on a challenging dataset of pottery objects, obtained from the publicly available Hampson collection.
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    GeoTopo: Dynamic 3D Facial Expression Retrieval Using Topological and Geometric Information
    (The Eurographics Association, 2014) Danelakis, Antonios; Theoharis, Theoharis; Pratikakis, Ioannis; Benjamin Bustos and Hedi Tabia and Jean-Philippe Vandeborre and Remco Veltkamp
    Recently, a lot of research has been dedicated to address the problem of facial expression recognition in dynamic sequences of 3D face scans. On the contrary, no research has been conducted on facial expression retrieval using dynamic 3D face scans. This paper illustrates the first results on the area of dynamic 3D facial expression retrieval. To this end, a novel descriptor is created, namely GeoTopo, capturing the topological as well as the geometric information of the 3D face scans along time. Experiments have been implemented using the angry, happy and surprise expressions of the publicly available dataset BU - 4DFE. The obtained retrieval results are very promising. Furthermore, a methodology which exploits the retrieval results, in order to achieve unsupervised dynamic 3D facial expression recognition, is presented. The aforementioned unsupervised methodology achieves classification accuracy comparable to the supervised dynamic 3D facial expression recognition state-of-the-art techniques.
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    Temporal Ensemble of Shape Functions
    (The Eurographics Association, 2014) Brkic, Karla; Aldomà, Aitor; Vincze, Markus; Segvic, Sinisa; Kalafatic, Zoran; Benjamin Bustos and Hedi Tabia and Jean-Philippe Vandeborre and Remco Veltkamp
    This paper proposes novel descriptors that integrate information from multiple views of a 3D object, called Temporal Ensemble of Shape Functions (TESF) descriptors. The TESF descriptors are built by combining per-view Ensemble of Shape Functions (ESF) descriptors with Spatio-Temporal Appearance (STA) descriptors. ESF descriptors provide a compact representation of ten different shape functions per object view (obtained by virtually rendering the object from different viewpoints), and STA descriptors efficiently combine ESF descriptors of multiple object views. The proposed descriptors are evaluated on two publicly available datasets, the 3D-Net database and the Princeton Shape Benchmark. They provide a good performance on both datasets, similar to that of the Spherical Harmonic Descriptor (SHD), with the advantage that because of their view-based nature the TESF descriptors might prove useful for the problem of object classification from limited viewpoints. Such property is of special interest in robotics where the agent is able to move around the object to improve single-view results.