3DOR 09

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/312

Browse

Browsing 3DOR 09 by Issue Date

Filter results by year or month
Now showing 1 - 12 of 12
  • Thumbnail Image
    Item
    Retrieval of 3D Articulated Objects Using a Graph-based Representation
    (The Eurographics Association, 2009) Agathos, Alexander; Pratikakis, Ioannis; Papadakis, Panagiotis; Perantonis, Stavros; Azariadis, Philip; Sapidis, Nickolas S.; Michela Spagnuolo and Ioannis Pratikakis and Remco Veltkamp and Theoharis Theoharis
    Most of the approaches which address the problem of 3D object retrieval, use global descriptors of the objects which fail to consistently compensate for the intra-class variability of articulated objects. In this paper, a retrieval methodology is presented which is based upon a graph-based object representation. This is composed of a meaningful new mesh segmentation along with a graph matching between the graph of the query object and each of the graphs that correspond to the objects of the 3D object database. The graph matching algorithm is based on the Earth Mover's Distance (EMD) similarity measure which is calculated using a new ground distance assignment. The superior performance of the proposed methodology is shown after an extensive experimentation comprising alternative descriptors for the constituent components of the 3D object as well as comparison with state of the art retrieval algorithms.
  • Thumbnail Image
    Item
    SHREC'09 Track: Structural Shape Retrieval on Watertight Models
    (The Eurographics Association, 2009) 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.; Michela Spagnuolo and Ioannis Pratikakis and Remco Veltkamp and Theoharis Theoharis
    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 on the retrieval of 3d models which exhibit a relevant similarity in the shape structure. Shape structure is typically characterised by features like protrusions, holes and concavities. It defines relationships in which components of the shape are connected.
  • Thumbnail Image
    Item
    Visual Vocabulary Signature for 3D Object Retrieval and Partial Matching
    (The Eurographics Association, 2009) Toldo, Roberto; Castellani, Umberto; Fusiello, Andrea; Michela Spagnuolo and Ioannis Pratikakis and Remco Veltkamp and Theoharis Theoharis
    In this paper a novel object signature is proposed for 3D object retrieval and partial matching. A part-based representation is obtained by partitioning the objects into subparts and by characterizing each segment with different geometric descriptors. Therefore, a Bag ofWords framework is introduced by clustering properly such descriptors in order to define the so called 3D visual vocabulary. In this fashion, the object signature is defined as a histogram of 3D visual word occurrences. Several examples on the Aim@Shape watertight dataset demonstrate the versatility of the proposed method in matching either 3D objects with articulated shape changes or partially occluded or compound objects. In particular, a comparison with the methods that participated to the Shape Retrieval contest 2007 (SHREC) reports satisfactory results for both object retrieval and partial matching.
  • Thumbnail Image
    Item
    A 3D Shape Benchmark for Retrieval and Automatic Classification of Architectural Data
    (The Eurographics Association, 2009) Wessel, Raoul; Blümel, Ina; Klein, Reinhard; Michela Spagnuolo and Ioannis Pratikakis and Remco Veltkamp and Theoharis Theoharis
    When drafting new buildings, architects make intensive use of existing 3D models including building elements, furnishing, and environment elements. These models are either directly included into the draft or serve as a source for inspiration. To allow efficient reuse of existing 3D models, shape retrieval methods considering the specific requirements of architects must be developed. Unfortunately, common 3D shape benchmarks which are used to evaluate the performance of retrieval algorithms are not well suited for architectural data. First, they incorporate models which are not related to this domain, and second and even more important, the provided classification schemes usually do not match an architect's intuition regarding their notion of design and function. To overcome these drawbacks, we present a freely downloadable shape benchmark especially designed for architectural 3D models. It currently contains 2257 objects from various content providers, including companies specialized on 3D CAD applications. All models are classified according to a scheme developed in close cooperation with architects taking into account their specific requirements regarding design and function. Additionally, we show retrieval results for this benchmark using unsupervised and supervised shape retrieval methods and discuss the specific problems regarding retrieval of architectural 3D models.
  • Thumbnail Image
    Item
    Partial Matching for Real Textured 3D Objects using Color Cubic Higher-order Local Auto-Correlation Features
    (The Eurographics Association, 2009) Kanezaki, Asako; Harada, T.; Kuniyoshi, Y.; Michela Spagnuolo and Ioannis Pratikakis and Remco Veltkamp and Theoharis Theoharis
    In recent years the need for the retrieval of real 3D objects is growing more and more. However, if the 3D models are obtained without the use of special equipment such as engineered environments or multi-camera systems, they are often incomplete, making retrieval difficult. On the other hand, real models often include rich texture information, which can compensate for the limited shape information. In this paper we present new 3D shape features which take into account an object's texture. We demonstrate the retrieval performance of these features on a dataset of real textured objects and a real color 3D scene.
  • Thumbnail Image
    Item
    SkelTre - Fast Skeletonisation for Imperfect Point Cloud Data of Botanic Trees
    (The Eurographics Association, 2009) Bucksch, Alexander; Lindenbergh, Roderik C.; Menenti, M.; Michela Spagnuolo and Ioannis Pratikakis and Remco Veltkamp and Theoharis Theoharis
    Terrestrial laser scanners capture 3D geometry as a point cloud. This paper reports on a new algorithm aiming at the skeletonisation of a laser scanner point cloud, representing a botanical tree without leafs. The resulting skeleton can subsequently be applied to obtain tree parameters like length and diameter of branches for botanic applications. Scanner-produced point cloud data are not only subject to noise, but also to undersampling and varying point densities, making it challenging to extract a topologically correct skeleton. The skeletonisation algorithm proposed in this paper consists of three steps: (i) extraction of a graph from an octree organization, (ii) reduction of the graph to the skeleton and (iii) embedding of the skeleton into the point cloud. The results are validated on laser scanner point clouds representing botanic trees. On a reference tree, the mean and maximal distance of the point cloud points to the skeleton could be reduced from 1.8 to 1.5 cm for the mean and from 15.6 to 10.5 cm for the maximum, compared to results from a previously developed method.
  • Thumbnail Image
    Item
    SHREC'09 Track: Generic Shape Retrieval
    (The Eurographics Association, 2009) Godil, A.; Dutagaci, H.; Akgül, C.; Axenopoulos, A.; Bustos, B.; Chaouch, M.; Daras, P.; Furuya, T.; Kreft, S.; Lian, Z.; Napoléon, T.; Mademlis, A.; Ohbuchi, R.; Rosin, P. L.; Sankur, B.; Schreck, Tobias; Sun, X.; Tezuka, M.; Verroust-Blondet, A.; Walter, M.; Yemez, Y.; Michela Spagnuolo and Ioannis Pratikakis and Remco Veltkamp and Theoharis Theoharis
    In this paper we present the results of the SHREC'09- Generic Shape Retrieval Contest. The aim of this track was to evaluate the performances of various 3D shape retrieval algorithms on the NIST generic shape benchmark. We hope that the NIST shape benchmark will provide valuable contributions to the 3D shape retrieval community. Seven groups have participated in the track and they have submitted 22 sets of rank lists based on different methods and parameters. The performance evaluation of the SHREC'09- Generic Shape Retrieval Contest is based on 6 different metrics.
  • Thumbnail Image
    Item
    Multilevel Relevance Feedback for 3D Shape Retrieval
    (The Eurographics Association, 2009) Giorgi, Daniela; Frosini, Patrizio; Spagnuolo, Michela; Falcidieno, Bianca; Michela Spagnuolo and Ioannis Pratikakis and Remco Veltkamp and Theoharis Theoharis
    Relevance feedback techniques are expected to play an important role in 3D search engines, as they help to bridge the semantic gap between the user and the system: similarity is a cognitive process, depending on the observer. We propose a novel relevance feedback technique, whose basic idea is threefold. First of all, the user is provided with a variety of shape descriptors, analysing different shape properties. The user then expresses her similarity concept through a friendly interface which supports multilevel relevance judgements. Finally, the system inhibits the role of the shape properties that do not reflect the user's idea of similarity. The assumption is that similarity may emerge as an inhibition of differences, i.e., as a lack of diversity with respect to the shape properties taken into account. The proposed technique is based on a simple scaling procedure, which does not require any a priori learning or optimization of parameters.
  • Thumbnail Image
    Item
    Template Based Shape Descriptor
    (The Eurographics Association, 2009) Rustamov, Raif M.; Michela Spagnuolo and Ioannis Pratikakis and Remco Veltkamp and Theoharis Theoharis
    We introduce a new 3D shape descriptor which maps the surface features onto an arbitrary template surface using mean-value interpolation. A compact numerical shape descriptor is extracted using manifold harmonics on the template. We show that mean-value interpolation is a strong alternative to the often used projection. The utility of using different templates is established by showing that concatenating descriptors coming from different templates improves retrieval quality.
  • Thumbnail Image
    Item
    SHREC'09 Track: Querying with Partial Models
    (The Eurographics Association, 2009) Dutagaci, H.; Godil, A.; Axenopoulos, A.; Daras, P.; Furuya, T.; Ohbuchi, R.; Michela Spagnuolo and Ioannis Pratikakis and Remco Veltkamp and Theoharis Theoharis
    The objective of the SHREC'09 Querying with Partial Models is to compare the performances of algorithms that accept a range image as the query and retrieve relevant 3D models from a database. The use of a range scan as the query addresses a real life scenario where the task of the system is to analyze a 3D scene and to identify what type of objects are present in the scene. Another benefit of developing retrieval algorithms based on range inputs is that they enable a simple 3D search interface composed of a desktop 3D scanner. Two groups have participated in the contest and have provided rank lists for the query set that is composed of range scans of 20 objects. This paper presents descriptions of the participants' methods and the results of the contest.
  • Thumbnail Image
    Item
    SHREC 2009 - Shape Retrieval Contest
    (The Eurographics Association, 2009) Veltkamp, Remco. C.; Haar, Frank B. ter; Michela Spagnuolo and Ioannis Pratikakis and Remco Veltkamp and Theoharis Theoharis
    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 organized in conjunction with the Eurographics Workshop on 3D Object Retrieval, where the evaluation results are presented. Thanks to the effort of previous track organizers,SHREC already provides many resources to compare and evaluate 3D retrieval methods. For this year's contest, we aimed at new and updated tracks. This time, three track were run: generic shape retrieval, querying with partial 3D models, and structural retrieval of watertight models.
  • Thumbnail Image
    Item
    Automatic 3D Facial Region Retrieval from Multi-pose Facial Datasets
    (The Eurographics Association, 2009) Perakis, Panagiotis; Theoharis, Theoharis; Passalis, Georgios; Kakadiaris, Ioannis A.; Michela Spagnuolo and Ioannis Pratikakis and Remco Veltkamp and Theoharis Theoharis
    The availability of 3D facial datasets is rapidly growing, mainly as a result of medical and biometric applications. These applications often require the retrieval of specific facial areas (such as the nasal region). The most crucial step in facial region retrieval is the detection of key 3D facial landmarks (e.g., the nose tip). A key advantage of 3D facial data over 2D facial data is their pose invariance. Any landmark detection method must therefore also be pose invariant. In this paper, we present the first 3D facial landmark detection method that works in datasets with pose rotations of up to 80 degree around the y-axis. It is tested on the largest publicly available 3D facial datasets, for which we have created a ground truth by manually annotating the 3D landmarks. Landmarks automatically detected by our method are then used to robustly retrieve facial regions from 3D facial datasets.