Neural networks for the recognition and pose estimation of 3D objects from a single 2D perspective view

摘要

In this paper we present a neural network (NN) based system for recognition and pose estimation of 3D objects from a single 2D perspective view. We develop an appearance based neural approach for this task. First the object is represented in a feature vector derived by a principal component network. Then a NN classifier trained with Resilient backpropagation (Rprop) algorithm is applied to identify it. Next pose parameters are obtained by four NN estimators trained on the same feature vector. Performance on recognition and pose estimation for real images under occlusions are shown. Comparative studies with two other approaches are carried out.