A Data-Driven Approach for Sketch-Based 3D Shape Retrieval via Similar Drawing-Style Recommendation

Abstract

Sketching is a simple and natural way of expression and communication for humans. For this reason, it gains increasing popularity in human computer interaction, with the emergence of multitouch tablets and styluses. In recent years, sketch-based interactive methods are widely used in many retrieval systems. In particular, a variety of sketch-based 3D model retrieval works have been presented. However, almost all of these works focus on directly matching sketches with the projection views of 3D models, and they suffer from the large differences between the sketch drawing and the views of 3D models, leading to unsatisfying retrieval results. Therefore, in this paper, during the matching procedure in the retrieval, we propose to match the sketch with each 3D model from historical users instead of projection views. Yet since the sketches between the current user and the historical users can have big difference, we also aim to handle users' personalized deviations and differences. To this end, we leverage recommendation algorithms to estimate the drawing style characteristic similarity between the current user and historical users. Experimental results on the Large Scale Sketch Track Benchmark(SHREC14LSSTB) demonstrate that our method outperforms several state-of-the-art methods.