Exploiting RGB-D data by means of convolutional neural networks (CNNs) is at the core of a number of robotics applications, including object detection, scene semantic segmentation, and grasping. We study novel approaches for learning deep representations from RGB-D data.

Relevant Publications:

L. Porzi, S. Rota Bulò, A. Penate-Sanchez, E.Ricci, F. Moreno-Noguer, “Learning Depth-aware Representations for Robotic Perception” Robotics and Automation Letters (RA-L), 2017.