Query by Partially-Drawn Sketches for 3D Shape Retrieval

Hand-drawn sketch is a powerful modality to query 3D shape models. However, specifying a detailed 3D shape by a sketch on the first try without reference (i.e., 3D model or real object) is difficult. In this paper, we aim at a sketch-based 3D shape retrieval system that tolerates coarsely drawn or incomplete sketches having small number of strokes. Such a system could be used to start a sketch-retrieve-refine interactive loop that could lead to a 3D shape having required shape details. Proposed algorithm uses deep feature embedding into common feature embedding space to compare sketches and 3D shape models. To handle coarse or incomplete sketches, a sketch, which is a sequence of strokes, is augmented by removing stroke for training a pair of DNNs to extract sketch features. A sketch feature is a fusion of an image based feature extracted by a convolutional neural network (CNN) and a 2D point sequence feature extracted by using a recurrent neural network (RNN). Embedding of 3D shape feature and the sketch feature is learned by using triplet loss. Experimental evaluation of the proposed method is performed using (simulated) incomplete sketches created by removing part of their strokes. The experiments show that sketch stroke removal augmentation significantly improved retrieval accuracy if queried by using such incomplete sketches.