Voxel-encoded descriptor for 3D model retrieval by exploring model’s spatial information

다국어 초록 (Multilingual Abstract)

Retrieving similar products with a given one has attracted considerable attention. However, products are usually assembled by multiplecomponents, frustrating the previous visual-based retrieval descriptors. We design a voxel-encoded descriptor (VED) by exploring models’spatial information, i.e., boundary data and internal data. This descriptor is computed in three steps. First, the posture of a polygonalmodel is normalized by improved voxel-based principal component analysis technique. Then, six color images are generated by projectingthe voxels along its six local axes. The color value of each pixel encodes status of all voxels intersecting with the ray starting from thepixel and parallel to the axis. The status of all voxels along a ray embodies the spatial distribution of the model along this ray. Finally, theVED is computed by applying 2D Fourier transformation to the six color images. With VED, we can distinguish a hollow sphere from asolid sphere. To improve the retrieval efficiency, the database structure is optimized by an improved geometric manifold entropy(iGEOMEN) scheme. VED and iGEOMEN are integrated into a model retrieval system. Experimental results demonstrate that the VEDdescriptor outperforms the previous visual-based shape descriptors, especially for complex assembly models.

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