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).

Paper

Manolis Savva, Fisher Yu, Hao Su, et al. SHREC’17 Track Large-Scale 3D Shape Retrieval from ShapeNet Core55 3DOR 2017

Citation

@inproceedings{savva2017large,
  title={Large-scale 3D shape retrieval from ShapeNet Core55: SHREC'17 track},
  author={Savva, Manolis and Yu, Fisher and Su, Hao and Kanezaki, Asako and Furuya, Takahiko and Ohbuchi, Ryutarou and Zhou, Zhichao and Yu, Rui and Bai, Song and Bai, Xiang and others},
  booktitle={Proceedings of the Workshop on 3D Object Retrieval},
  pages={39--50},
  year={2017}
}