  This work provides an architecture that incorporates depth and tactile
information to create rich and accurate 3D models useful for robotic
manipulation tasks. This is accomplished through the use of a 3D convolutional
neural network (CNN). Offline, the network is provided with both depth and
tactile information and trained to predict the object's geometry, thus filling
in regions of occlusion. At runtime, the network is provided a partial view of
an object. Tactile information is acquired to augment the captured depth
information. The network can then reason about the object's geometry by
utilizing both the collected tactile and depth information. We demonstrate that
even small amounts of additional tactile information can be incredibly helpful
in reasoning about object geometry. This is particularly true when information
from depth alone fails to produce an accurate geometric prediction. Our method
is benchmarked against and outperforms other visual-tactile approaches to
general geometric reasoning. We also provide experimental results comparing
grasping success with our method.
