3DOR: Eurographics Workshop on 3D Object Retrieval

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Browsing 3DOR: Eurographics Workshop on 3D Object Retrieval by Subject "Categories and Subject Descriptors"

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    Large-Scale 3D Shape Retrieval from ShapeNet Core55
    (The Eurographics Association, 2016) Savva, M.; Yu, F.; Su, Hao; Aono, M.; Chen, B.; Cohen-Or, D.; Deng, W.; Su, Hang; Bai, S.; Bai, X.; Fish, N.; Han, J.; Kalogerakis, E.; Learned-Miller, E. G.; Li, Y.; Liao, M.; Maji, S.; Tatsuma, A.; Wang, Y.; Zhang, N.; Zhou, Z.; A. Ferreira and A. Giachetti and D. Giorgi
    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 aims to provide a benchmark to evaluate large-scale shape retrieval based on the ShapeNet dataset. We use ShapeNet Core55, which provides more than 50 thousands models over 55 common categories in total for training and evaluating several algorithms. Five participating teams have submitted a variety of retrieval methods which were evaluated on several standard information retrieval performance metrics. We find the submitted methods work reasonably well on the track benchmark, but we also see significant space for improvement by future algorithms. We release all the data, results, and evaluation code for the benefit of the community.
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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).