DUAL INDEPENDENT CLASSIFICATION FOR SKETCH-BASED 3D SHAPE RETRIEVAL

Sketch-based 3D shape retrieval has received more attention by the pattern recognition and multimedia analysis community in the last few years. Sketches are simple to draw and could be an efficient tool for abstracting 3D models. However, it is very challenging to narrow the gap between 2D sketches and 3D models because of the discrepancy between their representations. Since the set of labels used to classify 3D shapes and 2D sketches is the same, the problem of sketch-based 3D shape retrieval may be reduced to a sketch classification problem and a 3D shape classification problem. By doing so, 3D shapes having the same label as the one predicted for a 2D sketch can be considered as relevant retrieved objects for the sketch. Nevertheless, the task of 2D sketch classification is also still challenging, particularly, due to the perception subjectivity of the designers, which increases intra-class variabilities. In this paper, we tackle the problem of sketch-based 3D shape retrieval and propose the dual independent classification solution which comprises two stages: First, we independently classify both 2D sketches and 3D shapes using deep neural networks. Then, we calculate pairwise cosine similarities between the softmax vectors of the query (2D sketch) and the target (3D shape). Our method has been evaluated on two commonly used datasets, namely SHREC13 and SHREC14. The results show that our method, despite its simplicity, has comparable and sometimes better performance when compared to state-of-the-art approaches.