  Self-supervised monocular depth estimation is of significant importance with
applications spanning across autonomous driving and robotics. However, the
reliance on self-supervision introduces a strong static-scene assumption,
thereby posing challenges in achieving optimal performance in dynamic scenes,
which are prevalent in most real-world situations. To address these issues, we
propose PPEA-Depth, a Progressive Parameter-Efficient Adaptation approach to
transfer a pre-trained image model for self-supervised depth estimation. The
training comprises two sequential stages: an initial phase trained on a dataset
primarily composed of static scenes, succeeded by an expansion to more
intricate datasets involving dynamic scenes. To facilitate this process, we
design compact encoder and decoder adapters to enable parameter-efficient
tuning, allowing the network to adapt effectively. They not only uphold
generalized patterns from pre-trained image models but also retain knowledge
gained from the preceding phase into the subsequent one. Extensive experiments
demonstrate that PPEA-Depth achieves state-of-the-art performance on KITTI,
CityScapes and DDAD datasets.
