Content-based 3-D model retrieval: A survey

As the number of available 3-D models grows, there is an increasing need to index and retrieve them according to their contents. This paper provides a survey of the up-to-date methods for content-based 3-D model retrieval. First, the new challenges encountered in 3-D model retrieval are discussed. Then, the system framework and some key techniques of content-based 3-D model retrieval are identified and explained, including canonical coordinate normalization and preprocessing, feature extraction, similarity match, query representation and user interface, and performance evaluation. In particular, similarity measures using semantic clues and machine learning methods, as well as retrieval approaches using nonshape features, are given adequate recognition as improvements and complements for traditional shapematching techniques. Typical 3-D model retrieval systems and search engines are also listed and compared. Finally, future research directions are indicated, and an extensive bibliography is provided.