3DOR 15

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/12281

Browse

Browsing 3DOR 15 by Subject "I.3.3 [Computer Graphics]"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Indoor Location Retrieval using Shape Matching of KinectFusion Scans to Large-Scale Indoor Point Clouds
    (The Eurographics Association, 2015) Al-Nuaimi, Anas; Piccolrovazzi, Martin; Gedikli, Suat; Steinbach, Eckehard; Schroth, Georg; I. Pratikakis and M. Spagnuolo and T. Theoharis and L. Van Gool and R. Veltkamp
    In this paper we show that indoor location retrieval can be posed as a part-in-whole matching problem of Kinect- Fusion (KinFu) query scans in large-scale target indoor point clouds. We tackle the problem with a local shape feature-based 3D Object Retrieval (3DOR) system. We specifically show that the KinFu queries suffer from artifacts stemming from the non-linear depth distortion and noise characteristics of Kinect-like sensors that are accentuated by the relative largeness of the queries. We furthermore show that proper calibration of the Kinect sensor using the CLAMS technique (Calibrating, Localizing, and Mapping, Simultaneously) proposed by Teichman et al. effectively reduces the artifacts in the generated KinFu scan and leads to a substantial retrieval performance boost. Throughout the paper we use queries and target point clouds obtained at the world's largest technical museum. The target point clouds cover floor spaces of up to 3500m2. We achieve an average localization accuracy of 6cm although the KinFu query scans make up only a tiny fraction of the target point clouds.
  • Thumbnail Image
    Item
    Retrieval of Non-rigid (textured) Shapes Using Low Quality 3D Models
    (The Eurographics Association, 2015) Giachetti, Andrea; Farina, Francesco; Fornasa, Francesco; Tatsuma, Atsushi; Sanada, Chika; Aono, Masaki; Biasotti, Silvia; Cerri, Andrea; Choi, Sungbin; I. Pratikakis and M. Spagnuolo and T. Theoharis and L. Van Gool and R. Veltkamp
    This paper reports the results of the SHREC 2015 track on retrieval of non-rigid (textured) shapes from low quality 3D models. This track has been organized to test the ability of the algorithms recently proposed by researchers for the retrieval of articulated and textured shapes to deal with real-world deformations and acquisition noise. For this reason we acquired with low cost devices models of plush toys lying on different sides on a platform, with articulated deformations and with different illumination conditions. We obtained in this way three novel and challenging datasets that have been used to organize a contest where the proposed task was the retrieval of istances of the same toy within acquired shapes collections, given a query model. The differences in datasets and tasks were related to the fact that one dataset was built without applying texture to shapes, and the others had texture applied to vertices with two different methods. We evaluated the retrieval results of the proposed techniques using standard evaluation measures: Precision-Recall curve; E-Measure; Discounted Cumulative Gain; Nearest Neighbor, First- Tier (Tier1) and Second-Tier (Tier2), Mean Average Precision. Robustness of methods against texture and shape deformation has also been separately evaluated.
  • Thumbnail Image
    Item
    Scalability of Non-Rigid 3D Shape Retrieval
    (The Eurographics Association, 2015) Sipiran, I.; Bustos, B.; Schreck, T.; Bronstein, A. M.; Bronstein, M.; Castellani, U.; Choi, S.; Lai, L.; Li, H.; Litman, R.; Sun, L.; I. Pratikakis and M. Spagnuolo and T. Theoharis and L. Van Gool and R. Veltkamp
    Due to recent advances in 3D acquisition and modeling, increasingly large amounts of 3D shape data become available in many application domains. This rises not only the need for effective methods for 3D shape retrieval, but also efficient retrieval and robust implementations. Previous 3D retrieval challenges have mainly considered data sets in the range of a few thousands of queries. In the 2015 SHREC track on Scalability of 3D Shape Retrieval we provide a benchmark with more than 96 thousand shapes. The data set is based on a non-rigid retrieval benchmark enhanced by other existing shape benchmarks. From the baseline models, a large set of partial objects were automatically created by simulating a range-image acquisition process. Four teams have participated in the track, with most methods providing very good to near-perfect retrieval results, and one less complex baseline method providing fair performance. Timing results indicate that three of the methods including the latter baseline one provide near- interactive time query execution. Generally, the cost of data pre-processing varies depending on the method.
  • Thumbnail Image
    Item
    Sketch-based 3D Object Retrieval Using Two Views and a Visual Part Alignment
    (The Eurographics Association, 2015) Yasseen, Zahraa; Verroust-Blondet, Anne; Nasri, Ahmad; I. Pratikakis and M. Spagnuolo and T. Theoharis and L. Van Gool and R. Veltkamp
    Hand drawn figures are the imprints of shapes in human's mind. How a human expresses a shape is a consequence of how he or she visualizes it. A query-by-sketch 3D object retrieval application is closely tied to this concept from two aspects. First, describing sketches must involve elements in a figure that matter most to a human. Second, the representative 2D projection of the target 3D objects must be limited to ''the canonical views'' from a human cognition perspective. We advocate for these two rules by presenting a new approach for sketch-based 3D object retrieval that describes a 2D shape by the visual protruding parts of its silhouette. Furthermore, the proposed approach computes estimations of ''part occlusion'' and ''symmetry'' in 2D shapes in a new paradigm for viewpoint selection that represents 3D objects by only the two views corresponding to the minimum value of each.