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.