With the rapid growth of available 3D models on the Internet, how to retrieve 3D models based on hand-drawn sketch retrieval are becoming increasingly important. This article proposes a new sketch-based 3D model retrieval approach. This approach is different from current methods that make use of low-level visual features to capture the search intention of users. The proposed method uses two kinds of semantic attributes, including pre-defined attributes and latent attributes. Specifically, pre-defined attributes are defined manually which can provide prior knowledge about different sketch categories and latent-attributes are more discriminative which can differentiate sketch categories at a finer level. Therefore, these semantic attributes can provide a more descriptive and discriminative meaningful representation than low-level feature descriptors. The experiment results demonstrate that this proposed method can achieve superior performance over previously proposed sketch-based 3D model retrieval methods.
A Comparative Evaluation of Learned Feature Descriptors on Hybrid Monocular Visual SLAM Methods