  In the field of self-supervised depth estimation, Convolutional Neural
Networks (CNNs) and Transformers have traditionally been dominant. However,
both architectures struggle with efficiently handling long-range dependencies
due to their local focus or computational demands. To overcome this limitation,
we present MambaDepth, a versatile network tailored for self-supervised depth
estimation. Drawing inspiration from the strengths of the Mamba architecture,
renowned for its adept handling of lengthy sequences and its ability to capture
global context efficiently through a State Space Model (SSM), we introduce
MambaDepth. This innovative architecture combines the U-Net's effectiveness in
self-supervised depth estimation with the advanced capabilities of Mamba.
MambaDepth is structured around a purely Mamba-based encoder-decoder framework,
incorporating skip connections to maintain spatial information at various
levels of the network. This configuration promotes an extensive feature
learning process, enabling the capture of fine details and broader contexts
within depth maps. Furthermore, we have developed a novel integration technique
within the Mamba blocks to facilitate uninterrupted connectivity and
information flow between the encoder and decoder components, thereby improving
depth accuracy. Comprehensive testing across the established KITTI dataset
demonstrates MambaDepth's superiority over leading CNN and Transformer-based
models in self-supervised depth estimation task, allowing it to achieve
state-of-the-art performance. Moreover, MambaDepth proves its superior
generalization capacities on other datasets such as Make3D and Cityscapes.
MambaDepth's performance heralds a new era in effective long-range dependency
modeling for self-supervised depth estimation.
