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We study the practical task of fine-grained 3D-VR-sketch-based 3D shaperetrieval. This task is of particular interest as 2D sketches were shown to beeffective queries for 2D images. However, due to the domain gap, it remainshard to achieve strong performance in 3D shape retrieval from 2D sketches.Recent work demonstrated the advantage of 3D VR sketching on this task. In ourwork, we focus on the challenge caused by inherent inaccuracies in 3D VRsketches. We observe that retrieval results obtained with a triplet loss with afixed margin value, commonly used for retrieval tasks, contain many irrelevantshapes and often just one or few with a similar structure to the query. Tomitigate this problem, we for the first time draw a connection between adaptivemargin values and shape similarities. In particular, we propose to use atriplet loss with an adaptive margin value driven by a "fitting gap", which isthe similarity of two shapes under structure-preserving deformations. We alsoconduct a user study which confirms that this fitting gap is indeed a suitablecriterion to evaluate the structural similarity of shapes. Furthermore, weintroduce a dataset of 202 VR sketches for 202 3D shapes drawn from memoryrather than from observation. The code and data are available athttps://github.com/Rowl1ng/Structure-Aware-VR-Sketch-Shape-Retrieval.

Structure-Aware 3D VR Sketch to 3D Shape Retrieval