An Overview of Semantics Processing in Content-Based 3D Model Retrieval

3D models are increasing greatly, and have been used in different fields. The need of retrieving 3D models is constantly emerging. Especially, how to reduce the `semantic gap' between the low-level features and high-level semantics, becomes one of the most hot topic. This paper gives a deep survey about the state of the art on semantic processing in content-based 3D model retrieval. Firstly, a framework of contend-based 3D model retrieval system integrated with high-level semantics is presented. Secondly, this paper concludes existing researches and divides the way of high-level semantic processing into three main categories: (1) using relevance feedback based on-line learning to integrate effectively users' high level semantic knowledge; (2) using off-line machine learning methods to narrow the gap between high-level semantic knowledge and low-level object representation; (3) using object ontology to define high-level concepts. Finally, the paper recommends some challenges in this field.