Object pose estimation by neural network

Demonstrates the ability of a back propagation neural network to estimate the pose of an object relative to a reference position. The network is trained with random views of wire frame objects as input and the rotation parameters of the views obtained from a CAD model of the object and camera as the target output. Orientation parameters of randomly generated test views are correctly generated