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 TangHaiqin Chen, Yan Wang, Qunying Zhou, Ji Zhang, Hongyuan Wang

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)104-115
Number of pages12
JournalComputers and Graphics (Pergamon)
StatePublished - Oct 2022


  • Cross-modality retrieval
  • Point cloud classification
  • Shape retrieval in the wild
  • Sketch-based 3D shape retrieval

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • General Engineering
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design


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