  We present a system for keyframe-based dense camera tracking and depth map
estimation that is entirely learned. For tracking, we estimate small pose
increments between the current camera image and a synthetic viewpoint. This
significantly simplifies the learning problem and alleviates the dataset bias
for camera motions. Further, we show that generating a large number of pose
hypotheses leads to more accurate predictions. For mapping, we accumulate
information in a cost volume centered at the current depth estimate. The
mapping network then combines the cost volume and the keyframe image to update
the depth prediction, thereby effectively making use of depth measurements and
image-based priors. Our approach yields state-of-the-art results with few
images and is robust with respect to noisy camera poses. We demonstrate that
the performance of our 6 DOF tracking competes with RGB-D tracking algorithms.
We compare favorably against strong classic and deep learning powered dense
depth algorithms.
