View subspaces for indexing and retrieval of 3d models

View-based indexing schemes for 3D object retrieval is gaining popularity since they provide good retrieval results. These schemes are coherent with the theory that humans recognize objects based on their 2D appearances. The view-based techniques also allow users to search with various queries such as binary images, range images and even 2D sketches. The previous view-based techniques use classical 2D shape descriptors such as Fourier invariants, Zernike moments, SIFT-based local features and 2D DFT coefficients. These methods describe each object independent of others. In this work, we explore data driven subspace models, such as PCA, ICA and NMF to describe the shape information of the views. We treat the depth images obtained from various points of the view sphere as 2D intensity images and train a subspace to extract the inherent structure of the views within a database