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 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).
Description

        
@inproceedings{
10.2312:3dor.20171050
, booktitle = {
Eurographics Workshop on 3D Object Retrieval
}, editor = {
Ioannis Pratikakis and Florent Dupont and Maks Ovsjanikov
}, title = {{
Large-Scale 3D Shape Retrieval from ShapeNet Core55
}}, author = {
Savva, Manolis
and
Yu, Fisher
and
Aono, Masaki
and
Tatsuma, Atsushi
and
Thermos, S.
and
Axenopoulos, A.
and
Papadopoulos, G. Th.
and
Daras, P.
and
Deng, Xiao
and
Lian, Zhouhui
and
Li, Bo
and
Johan, Henry
and
Su, Hao
and
Lu, Yijuan
and
Mk, Sanjeev
and
Kanezaki, Asako
and
Furuya, Takahiko
and
Ohbuchi, Ryutarou
and
Zhou, Zhichao
and
Yu, Rui
and
Bai, Song
and
Bai, Xiang
}, year = {
2017
}, publisher = {
The Eurographics Association
}, ISSN = {
1997-0471
}, ISBN = {
978-3-03868-030-7
}, DOI = {
10.2312/3dor.20171050
} }
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