OrthographicNet: A Deep Transfer Learning Approach for 3-D Object Recognition in Open-Ended Domains

Nowadays, service robots are appearing more and more in our daily life. For this type of robot, open-ended object category learning and recognition is necessary since no matter how extensive the training data used for batch learning, the robot might be faced with a
new object
when operating in a real-world environment. In this article, we present
OrthographicNet
, a convolutional neural network based model, for 3-D object recognition in open-ended domains. In particular,
OrthographicNet
generates a global rotation- and scale-invariant representation for a given 3-D object, enabling robots to recognize the same or similar objects seen from different perspectives. Experimental results show that our approach yields significant improvements over the previous state-of-the-art approaches concerning object recognition performance and scalability in open-ended scenarios. Moreover,
OrthographicNet
demonstrates the capability of learning new categories from very few examples on-site. Regarding real-time performance, three real-world demonstrations validate the promising performance of the proposed architecture.