Sketch-based 3D shape retrieval (SBSR) is an important yet challenging task, which has drawn more and more attention in recent years. Existing approaches address the problem in a restricted setting, without appropriately simulating real application scenarios. To mimic the realistic setting, in this track, we adopt large-scale sketches drawn by amateurs of different levels of drawing skills, as well as a variety of 3D shapes including not only CAD models but also models scanned from real objects. We define two SBSR tasks and construct two benchmarks consisting of more than 46,000 CAD models, 1,700 realistic models, and 145,000 sketches in total. Four teams participated in this track and submitted 15 runs for the two tasks, evaluated by 7 commonly-adopted metrics. We hope that, the benchmarks, the comparative results, and the open-sourced evaluation code will foster future research in this direction among the 3D object retrieval community.
@article{arxiv.2207.04945,
title = {SHREC'22 Track: Sketch-Based 3D Shape Retrieval in the Wild},
author = {Jie Qin and Shuaihang Yuan and Jiaxin Chen and Boulbaba Ben Amor and Yi Fang and Nhat Hoang-Xuan and Chi-Bien Chu and Khoi-Nguyen Nguyen-Ngoc and Thien-Tri Cao and Nhat-Khang Ngo and Tuan-Luc Huynh and Hai-Dang Nguyen and Minh-Triet Tran and Haoyang Luo and Jianning Wang and Zheng Zhang and Zihao Xin and Yang Wang and Feng Wang and Ying Tang and Haiqin Chen and Yan Wang and Qunying Zhou and Ji Zhang and Hongyuan Wang},
journal= {arXiv preprint arXiv:2207.04945},
year = {2022}
}