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.

本文提出了一种基于关键帧的密集相机跟踪和深度图估计系统，该系统完全是通过学习而实现的，实现了小姿态增量的估计和大量姿态假设的产生，并通过成本体积累积信息和图像先验来更新深度预测，具有优越的 RGB-D 跟踪算法性能和与强大的传统和深度学习算法比较的竞争力。