With the rapid growth of available 3D models, fast retrieval of suitable 3D models has become a crucial task for industrial applications. This paper proposes a novel sketch-based 3D model retrieval approach which utilizes both global feature-based and local feature-based techniques. Unlike current approaches which use either global or local features, as well as do not take into account semantic relations between local features, we extract these two kinds of feature information from the representative 2D views of 3D models that can facilitate semantic description and retrieval for 3D models. Global features represent the gross exterior boundary shape information, and local features describe the interior details by compact visual words. Specifically, an improved bag-of-features method is provided to extract local features and their latent semantic relations. In addition, an efficient two-stage matching strategy is used to measure the distance between the query sketch and 3D models for selection and refinement. Experiment results demonstrate that our approach which combines these two kinds of complementary features significantly outperforms several state-of-the-art approaches. 
A new sketch-based 3D model retrieval approach by using global and local features