SHREC'22 Track: Sketch-Based 3D Shape Retrieval in the Wild
This work addresses the challenge of sketch-based 3D shape retrieval for the 3D object retrieval community by introducing benchmarks that simulate real-world scenarios, though it is incremental as it builds on existing tasks with new data.
The paper tackled the problem of sketch-based 3D shape retrieval in realistic settings by creating large-scale benchmarks with over 46,000 CAD models, 1,700 realistic models, and 145,000 sketches drawn by amateurs, and evaluated four teams' submissions using seven metrics to provide comparative results.
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.