  Many approaches have been proposed to estimate camera poses by directly
minimizing photometric error. However, due to the non-convex property of direct
alignment, proper initialization is still required for these methods. Many
robust norms (e.g. Huber norm) have been proposed to deal with the outlier
terms caused by incorrect initializations. These robust norms are solely
defined on the magnitude of each error term. In this paper, we propose a novel
robust norm, named FlowNorm, that exploits the information from both the local
error term and the global image registration information. While the local
information is defined on patch alignments, the global information is estimated
using a learning-based network. Using both the local and global information, we
achieve an unprecedented convergence range in which images can be aligned given
large view angle changes or small overlaps. We further demonstrate the
usability of the proposed robust norm by integrating it into the direct methods
DSO and BA-Net, and generate more robust and accurate results in real-time.
