A layered network for the correspondence of 3D objects

A computational approach for solving the correspondence problem between different views of objects in range images is presented. This is modeled as a layered constraint satisfaction network which can be implemented on a parallel analog neural network. In this approach, each view of an object is represented by an attributed graph with nodes as surfaces and their bounding vertices, and links as relations between adjacent surfaces. The matching strategy is a two-step process. Each step is formulated with a constraint satisfaction network, and implemented on a Hopfield network. At each level, a set of local, adjacency and global constraints is specified, and an appropriate energy function to be minimized is defined. At the first level of this hierarchy, surface patches are matched and clusters of rotation transformations are hypothesized. At the second level, the computed rotation transformation is applied to the corresponding vertices, and the translation vector is computed