  While the keypoint-based maps created by sparse monocular simultaneous
localisation and mapping (SLAM) systems are useful for camera tracking, dense
3D reconstructions may be desired for many robotic tasks. Solutions involving
depth cameras are limited in range and to indoor spaces, and dense
reconstruction systems based on minimising the photometric error between frames
are typically poorly constrained and suffer from scale ambiguity. To address
these issues, we propose a 3D reconstruction system that leverages the output
of a convolutional neural network (CNN) to produce fully dense depth maps for
keyframes that include metric scale.
  Our system, DeepFusion, is capable of producing real-time dense
reconstructions on a GPU. It fuses the output of a semi-dense multiview stereo
algorithm with the depth and gradient predictions of a CNN in a probabilistic
fashion, using learned uncertainties produced by the network. While the network
only needs to be run once per keyframe, we are able to optimise for the depth
map with each new frame so as to constantly make use of new geometric
constraints. Based on its performance on synthetic and real-world datasets, we
demonstrate that DeepFusion is capable of performing at least as well as other
comparable systems.
