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.

自监督单目深度估计在自动驾驶和机器人领域具有重要意义。本文提出了一种渐进参数高效适应的方法，通过转移预训练的图像模型来实现自监督深度估计，解决了动态场景中的性能优化挑战，并通过在 KITTI、CityScapes 和 DDAD 数据集上进行广泛实验，取得了最先进的效果。