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A 3D Content Retrieval system is a computer system for browsing, searching and retrieving three dimensional digital contents (e.g.: Computer-aided design, molecular biology models, and cultural heritage 3D scenes, etc.) from a large database of digital images. The most original way of doing 3D content retrieval uses methods to add description text to 3D content files such as the content file name, link text, and the web page title so that related 3D content can be found through text retrieval. Because of the inefficiency of manually annotating 3D files, researchers have investigated ways to automate the annotation process and provide a unified standard to create text descriptions for 3D contents. Moreover, the increase in 3D content has demanded and inspired more advanced ways to retrieve 3D information. Thus, shape matching methods for 3D content retrieval have become popular. Shape matching retrieval is based on techniques that compare and contrast similarities between 3D models. ==3D retrieval methods== Derive a high level description (e.g.: a skeleton) and then find matching results This method describes 3D models by using a skeleton. The skeleton encodes the geometric and topological information in the form of a skeletal graph and uses graph matching techniques to match the skeletons and compare them.〔Sundar, H., Silver, D., Gagvani, N., Dickinson, S., Skeleton based shape matching and retrieval, In: Proc. SMI, Seoul, Korea (2003)〕 However, this method requires a 2-manifold input model, and it is very sensitive to noise and details. Many of the existing 3D models are created for visualization purposes, while missing the input quality standard for the skeleton method. The skeleton 3D retrieval method needs more time and effort before it can be used widely. Compute a feature vector based on statistics Unlike Skeleton modeling, which requires a high quality standard for the input source, statistical methods do not put restriction on the validity of an input source. Shape histograms, feature vectors composed of global geo-metic properties such as circularity and eccentricity, and feature vectors created using frequency decomposition of spherical functions are common examples of using statistical methods to describe 3D information.〔Min, P., Kazhdan, M., Funkhouser, T., A comparison of text and shape matching for retrieval of Online 3D models. Research And Advanced Technology For Digital Libraries, 2004, Vol.3232, pp.209-220〕 2D projection method Some approaches use 2D projections of a 3D model, justified by the assumption that if two objects are similar in 3D, then they should have similar 2D projections in many directions. Prototypical Views〔Cyr, C.M., Kimia, B.B., 3D object recognition using shape similarity-based aspect graph, In: Proc. ICCV, IEEE (2001)〕 and Light field description〔Chen, D.Y., Ouhyoung, M., Tian, X.P., Shen, Y.T., Ouhyoung, M., On visual similarity based 3D model retrieval, In: Proc. Eurographics, Granada, Spain (2003)〕 are good examples of 2D projection methods. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「3D Content Retrieval」の詳細全文を読む スポンサード リンク
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