SHREC'22 Track: Sketch-Based 3D Shape Retrieval in the Wild

TL;DR

This paper introduces large-scale benchmarks and evaluates multiple approaches for sketch-based 3D shape retrieval in realistic scenarios involving amateur sketches and diverse 3D models, aiming to advance research in practical applications.

Contribution

It presents new large-scale benchmarks with real-world sketches and models, along with evaluation results, to better simulate practical retrieval scenarios.

Findings

Four teams participated with 15 runs evaluated on seven metrics.

The benchmarks include over 46,000 CAD models, 1,700 realistic models, and 145,000 sketches.

Open-sourced evaluation code is provided to facilitate future research.

Abstract

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.