  Unsupervised monocular trained depth estimation models make use of adjacent
frames as a supervisory signal during the training phase. However, temporally
correlated frames are also available at inference time for many clinical
applications, e.g., surgical navigation. The vast majority of monocular systems
do not exploit this valuable signal that could be deployed to enhance the depth
estimates. Those that do, achieve only limited gains due to the unique
challenges in endoscopic scenes, such as low and homogeneous textures and
inter-frame brightness fluctuations. In this work, we present SMUDLP, a novel
and unsupervised paradigm for multi-frame monocular endoscopic depth
estimation. The SMUDLP integrates a learnable patchmatch module to adaptively
increase the discriminative ability in low-texture and homogeneous-texture
regions, and enforces cross-teaching and self-teaching consistencies to provide
efficacious regularizations towards brightness fluctuations. Our detailed
experiments on both SCARED and Hamlyn datasets indicate that the SMUDLP exceeds
state-of-the-art competitors by a large margin, including those that use single
or multiple frames at inference time. The source code and trained models will
be publicly available upon the acceptance.
