@inproceedings{56a0ac00d64a42ce99b7e4fb7a09ce02,
title = "SHREC'16 track large-scale 3D shape retrieval from ShapeNet Core55",
abstract = "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.",
author = "M. Savva and F. Yu and Hao Su and M. Aono and B. Chen and D. Cohen-Or and W. Deng and Hang Su and S. Bai and X. Bai and N. Fish and J. Han and E. Kalogerakis and Learned-Miller, \{E. G.\} and Y. Li and M. Liao and S. Maji and A. Tatsuma and Y. Wang and N. Zhang and Z. Zhou",
note = "Publisher Copyright: {\textcopyright} 2016 The Eurographics Association.; 9th Eurographics Workshop on 3D Object Retrieval, 3DOR 2016 ; Conference date: 08-05-2016",
year = "2016",
doi = "10.2312/3dor.20161092",
language = "الإنجليزيّة",
series = "Eurographics Workshop on 3D Object Retrieval, EG 3DOR",
publisher = "Eurographics Association",
pages = "89--98",
editor = "Alfredo Ferreira and Daniela Giorgi and Andrea Giachetti",
booktitle = "EG 3DOR 2016 - Eurographics 2016 Workshop on 3D Object Retrieval",
}