3DOR 17

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https://diglib.eg.org/handle/10.2312/2631242

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Browsing 3DOR 17 by Subject "H.3.3 [Computer Graphics]"

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    Large-Scale 3D Shape Retrieval from ShapeNet Core55
    (The Eurographics Association, 2017) Savva, Manolis; Yu, Fisher; Su, Hao; Kanezaki, Asako; Furuya, Takahiko; Ohbuchi, Ryutarou; Zhou, Zhichao; Yu, Rui; Bai, Song; Bai, Xiang; Aono, Masaki; Tatsuma, Atsushi; Thermos, S.; Axenopoulos, A.; Papadopoulos, G. Th.; Daras, P.; Deng, Xiao; Lian, Zhouhui; Li, Bo; Johan, Henry; Lu, Yijuan; Mk, Sanjeev; Ioannis Pratikakis and Florent Dupont and Maks Ovsjanikov
    With the advent of commodity 3D capturing devices and better 3D modeling tools, 3D shape content is becoming increasingly prevalent. Therefore, the need for shape retrieval algorithms to handle large-scale shape repositories is more and more important. This track provides a benchmark to evaluate large-scale 3D shape retrieval based on the ShapeNet dataset. It is a continuation of the SHREC 2016 large-scale shape retrieval challenge with a goal of measuring progress with recent developments in deep learning methods for shape retrieval. We use ShapeNet Core55, which provides more than 50 thousands models over 55 common categories in total for training and evaluating several algorithms. Eight participating teams have submitted a variety of retrieval methods which were evaluated on several standard information retrieval performance metrics. The approaches vary in terms of the 3D representation, using multi-view projections, point sets, volumetric grids, or traditional 3D shape descriptors. Overall performance on the shape retrieval task has improved significantly compared to the iteration of this competition in SHREC 2016. We release all data, results, and evaluation code for the benefit of the community and to catalyze future research into large-scale 3D shape retrieval (website: https://www.shapenet.org/shrec17).
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    Point-Cloud Shape Retrieval of Non-Rigid Toys
    (The Eurographics Association, 2017) Limberger, F. A.; Wilson, R. C.; Aono, M.; Audebert, N.; Boulch, A.; Bustos, B.; Giachetti, A.; Godil, A.; Saux, B. Le; Li, B.; Lu, Y.; Nguyen, H.-D.; Nguyen, V.-T.; Pham, V.-K.; Sipiran, I.; Tatsuma, A.; Tran, M.-T.; Velasco-Forero, S.; Ioannis Pratikakis and Florent Dupont and Maks Ovsjanikov
    In this paper, we present the results of the SHREC'17 Track: Point-Cloud Shape Retrieval of Non-Rigid Toys. The aim of this track is to create a fair benchmark to evaluate the performance of methods on the non-rigid point-cloud shape retrieval problem. The database used in this task contains 100 3D point-cloud models which are classified into 10 different categories. All point clouds were generated by scanning each one of the models in their final poses using a 3D scanner, i.e., all models have been articulated before scanned. The retrieval performance is evaluated using seven commonly-used statistics (PR-plot, NN, FT, ST, E-measure, DCG, mAP). In total, there are 8 groups and 31 submissions taking part of this contest. The evaluation results shown by this work suggest that researchers are in the right way towards shape descriptors which can capture the main characteristics of 3D models, however, more tests still need to be made, since this is the first time we compare non-rigid signatures for point-cloud shape retrieval.