Now showing items 21-40 of 200

    • SHREC'09 Track: Structural Shape Retrieval on Watertight Models 

      Hartveldt, J.; Spagnuolo, M.; Axenopoulos, A.; Biasotti, S.; Daras, P.; Dutagaci, H.; Furuya, T.; Godil, A.; Li, X.; Mademlis, A.; Marini, S.; Napoleon, T.; Ohbuchi, R.; Tezuka, M. (The Eurographics Association, 2009)
      The annual SHape REtrieval Contest (SHREC) measures the performance of 3D model retrieval methods for several different types of models and retrieval purposes. In this contest the structural shape retrieval track focuses ...
    • SHREC 2009 - Shape Retrieval Contest 

      Veltkamp, Remco. C.; Haar, Frank B. ter (The Eurographics Association, 2009)
      The general objective of the 3D Shape Retrieval Contest (see http://www.aimatshape.net/event/ SHREC) is to evaluate the effectiveness of 3D-shape retrieval algorithms. After three years of success, the contest is now ...
    • A Robust 3D Interest Points Detector Based on Harris Operator 

      Sipiran, Ivan; Bustos, Benjamin (The Eurographics Association, 2010)
      With the increasing amount of 3D data and the ability of capture devices to produce low-cost multimedia data, the capability to select relevant information has become an interesting research field. In 3D objects, the aim ...
    • SHREC'10 Track: Protein Model Classification 

      Mavridis, L.; Venkatraman, V.; Ritchie, D. W.; Morikawa, N.; Andonov, R.; Cornu, A.; Malod-Dognin, N.; Nicolas, J.; Temerinac-Ott, M.; Reisert, M.; Burkhardt, H.; Axenopoulos, A.; Daras, P. (The Eurographics Association, 2010)
      This paper presents the results of the 3D Shape Retrieval Contest 2010 (SHREC'10) track Protein Models Classification. The aim of this track is to evaluate how well 3D shape recognition algorithms can classify protein ...
    • SHREC'10 Track: Feature Detection and Description 

      Bronstein, A. M.; Bronstein, M. M.; Bustos, B.; Castellani, U.; Crisani, M.; Falcidieno, B.; Guibas, L. J.; Kokkinos, I.; Murino, V.; Ovsjanikov, M.; Patané, G.; Sipiran, I.; Spagnuolo, M.; Sun, J. (The Eurographics Association, 2010)
      Feature-based approaches have recently become very popular in computer vision and image analysis applications, and are becoming a promising direction in shape retrieval. The SHREC'10 feature detection and description ...
    • SHREC'10 Track: Range Scan Retrieval 

      Dutagaci, H.; Godil, A.; Cheung, C. P.; Furuya, T.; Hillenbrand, U.; Ohbuchi, R. (The Eurographics Association, 2010)
      The 3D Shape Retrieval Contest 2010 (SHREC'10) on range scan retrieval aims at comparing algorithms that match a range scan to complete 3D models in a target database. The queries are range scans of real objects, and the ...
    • Feature Selection for Enhanced Spectral Shape Comparison 

      Marini, Simone; Patané, Giuseppe; Spagnuolo, Michela; Falcidieno, Bianca (The Eurographics Association, 2010)
      In the context of shape matching, this paper proposes a framework for selecting the Laplacian eigenvalues of 3D shapes that are more relevant for shape comparison and classification. Three approaches are compared to identify ...
    • SHREC'10 Track: Robust Shape Retrieval 

      Bronstein, A. M.; Bronstein, M. M.; Castellani, U.; Falcidieno, B.; Fusiello, A.; Godil, A.; Guibas, L. J.; Kokkinos, I.; Lian, Z.; Ovsjanikov, M.; Patané, G.; Spagnuolo, M.; Toldo, R. (The Eurographics Association, 2010)
      The 3D Shape Retrieval Contest 2010 (SHREC'10) robust shape retrieval benchmark simulates a retrieval scenario, in which the queries include multiple modifications and transformations of the same shape. The benchmark allows ...
    • SHREC'10 Track: Non-rigid 3D Shape Retrieval 

      Lian, Z.; Godil, A.; Fabry, T.; Furuya, T.; Hermans, J.; Ohbuchi, R.; Shu, C.; Smeets, D.; Suetens, P.; Vandermeulen, D.; Wuhrer, S. (The Eurographics Association, 2010)
      Non-rigid shape matching is one of the most challenging fields in content-based 3D object retrieval. The aim of the 3D Shape Retrieval Contest 2010 (SHREC'10) track on non-rigid 3D shape retrieval is to evaluate and compare ...
    • Robust Volumetric Shape Descriptor 

      Rustamov, Raif M. (The Eurographics Association, 2010)
      This paper introduces a volume-based shape descriptor that is robust with respect to changes in pose and topology. We use modified shape distributions of [OFCD02] in conjunction with the interior distances and barycentroid ...
    • The Fast Reject Schema for Part-in-Whole 3D Shape Matching 

      Attene, Marco; Marini, Simone; Spagnuolo, Michela; Falcidieno, Bianca (The Eurographics Association, 2010)
      This paper proposes a new framework for an efficient detection of template shapes within a target 3D model, or scene. The proposed approach distinguishes from the previous literature because the part-in-whole matching ...
    • SHREC'10 Track: Generic 3D Warehouse 

      Vanamali, T. P.; Godil, A.; Dutagaci, H.; Furuya, T.; Lian, Z.; Ohbuchi, R. (The Eurographics Association, 2010)
      In this paper we present the results of the 3D Shape Retrieval Contest 2010 (SHREC'10) track Generic 3D Warehouse. The aim of this track was to evaluate the performances of various 3D shape retrieval algorithms on a large ...
    • Semantics-Driven Approach for Automatic Selection of Best Views of 3D Shapes 

      Laga, Hamid (The Eurographics Association, 2010)
      We introduce a new framework for the automatic selection of the best views of 3D models. The approach is based on the assumption that models belonging to the same class of shapes share the same salient features that ...
    • SHREC'10 Track: Large Scale Retrieval 

      Veltkamp, Remco C.; Giezeman, Geert-Jan; Bast, Hannah; Baumbach, Thomas; Furuya, Takahiko; Giesen, Joachim; Godil, Afzal; Lian, Zhouhui; Ohbuchi, Ryutarou; Saleem, Waqar (The Eurographics Association, 2010)
      This paper is a report on the 3D Shape Retrieval Constest 2010 (SHREC'10) track on large scale retrieval. This benchmark allows evaluating how wel retrieval algorithms scale up to large collections of 3D models. The task ...
    • Learning the Compositional Structure of Man-Made Objects for 3D Shape Retrieval 

      Wessel, Raoul; Klein, Reinhard (The Eurographics Association, 2010)
      While approaches based on local features play a more and more important role for 3D shape retrieval, the problems of feature selection and similarity measurement between sets of local features still remain open tasks. ...
    • SHREC'10 Track: Correspondence Finding 

      Bronstein, A. M.; Bronstein, M. M.; Castellani, U.; Dubrovina, A.; Guibas, L. J.; Horaud, R. P.; Kimmel, R.; Knossow, D.; Lavante, E. von; Mateus, D.; Ovsjanikov, M.; Sharma, A. (The Eurographics Association, 2010)
      The SHREC'10 correspondence finding benchmark simulates a one-to-one shape matching scenario, in which one of the shapes undergoes multiple modifications and transformations. The benchmark allows evaluating how correspondence ...
    • Fast Human Classification of 3D Object Benchmarks 

      Jagadeesan, A. P.; Wenzel, J.; Corney, Jonathan R.; Yan, X.; Sherlock, A.; Torres-Sanchez, C.; Regli, William (The Eurographics Association, 2010)
      Although a significant number of benchmark data sets for 3D object based retrieval systems have been proposed over the last decade their value is dependent on a robust classification of their content being available. Ideally ...
    • Person Independent 3D Facial Expression Recognition by a Selected Ensemble of SIFT Descriptors 

      Berretti, Stefano; Amor, Boulbaba Ben; Daoudi, Mohamed; Bimbo, Alberto Del (The Eurographics Association, 2010)
      Facial expression recognition has been addressed mainly working on 2D images or videos. In this paper, the problem of person-independent facial expression recognition is addressed on 3D shapes. To this end, an original ...
    • Real-time Expression Recognition from Dynamic Sequences of 3D Facial Scans 

      Berretti, Stefano; Bimbo, Alberto del; Pala, Pietro (The Eurographics Association, 2012)
      In this paper, we address the problem of person-independent facial expression recognition in dynamic sequences of 3D face scans. To this end, an original approach is proposed that relies on automatically extracting a set ...
    • SHREC'12 Track: 3D Mesh Segmentation 

      Lavoué, G.; Vandeborre, J-P.; Benhabiles, H.; Daoudi, M.; Huebner, K.; Mortara, M.; Spagnuolo, M. (The Eurographics Association, 2012)
      3D mesh segmentation is a fundamental process in many applications such as shape retrieval, compression, deformation, etc. The objective of this track is to evaluate the performance of recent segmentation methods using a ...