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SHREC’22 track: Sketch-based 3D shape retrieval in the wild

  • Jie Qin*
  • , Shuaihang Yuan
  • , Jiaxin Chen
  • , Boulbaba Ben Amor
  • , Yi Fang
  • , Nhat Hoang-Xuan
  • , Chi Bien Chu
  • , Khoi Nguyen Nguyen-Ngoc
  • , Thien Tri Cao
  • , Nhat Khang Ngo
  • , Tuan Luc Huynh
  • , Hai Dang Nguyen
  • , Minh Triet Tran
  • , Haoyang Luo
  • , Jianning Wang
  • , Zheng Zhang
  • , Zihao Xin
  • , Yang Wang
  • , Feng Wang
  • , Ying Tang
  • Haiqin Chen, Yan Wang, Qunying Zhou, Ji Zhang, Hongyuan Wang
*此作品的通讯作者
  • Nanjing University of Aeronautics and Astronautics
  • New York University
  • Dépt. Énergétique Industrielle
  • Inception Institute of Artificial Intelligence
  • New York University Abu Dhabi
  • Vietnam National University Ho Chi Minh City
  • Harbin Institute of Technology
  • Changzhou University

科研成果: 期刊稿件文章同行评审

摘要

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, 1700 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.

源语言英语
页(从-至)104-115
页数12
期刊Computers and Graphics
107
DOI
出版状态已出版 - 10月 2022

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