Li, BoLu, YijuanDuan, FuqingDong, ShuilongFan, YachunQian, LuLaga, HamidLi, HaishengLi, YuxiangLiu, PengOvsjanikov, MaksTabia, HediYe, YuxiangYin, HuanpuXue, ZiyuA. Ferreira and A. Giachetti and D. Giorgi2016-05-042016-05-042016978-3-03868-004-81997-0471https://doi.org/10.2312/3dor.20161087Sketch-based 3D shape retrieval has unique representation availability of the queries and vast applications. Therefore, it has received more and more attentions in the research community of content-based 3D object retrieval. However, sketch-based 3D shape retrieval is a challenging research topic due to the semantic gap existing between the inaccurate representation of sketches and accurate representation of 3D models. In order to enrich and advance the study of sketch-based 3D shape retrieval, we initialize the research on 3D sketch-based 3D model retrieval and collect a 3D sketch dataset based on a developed 3D sketching interface which facilitates us to draw 3D sketches in the air while standing in front of a Microsoft Kinect. The objective of this track is to evaluate the performance of different 3D sketch-based 3D model retrieval algorithms using the hand-drawn 3D sketch query dataset and a generic 3D model target dataset. The benchmark contains 300 sketches that are evenly divided into 30 classes, as well as 1258 3D models that are classified into 90 classes. In this track, nine runs have been submitted by five groups and their retrieval performance has been evaluated using seven commonly used retrieval performance metrics.We wish this benchmark, the comparative evaluation results and the corresponding evaluation code will further promote sketch-based 3D shape retrieval and its applications.H.3.3 [Computer Graphics]Information SystemsInformation Search and Retrieval10.2312/3dor.2016108747-54