  Self-supervised monocular depth estimation has garnered considerable
attention for its applications in autonomous driving and robotics. While recent
methods have made strides in leveraging techniques like the Self Query Layer
(SQL) to infer depth from motion, they often overlook the potential of
strengthening pose information. In this paper, we introduce SPIdepth, a novel
approach that prioritizes enhancing the pose network for improved depth
estimation. Building upon the foundation laid by SQL, SPIdepth emphasizes the
importance of pose information in capturing fine-grained scene structures. By
enhancing the pose network's capabilities, SPIdepth achieves remarkable
advancements in scene understanding and depth estimation. Experimental results
on benchmark datasets such as KITTI, Cityscapes, and Make3D showcase SPIdepth's
state-of-the-art performance, surpassing previous methods by significant
margins. Specifically, SPIdepth tops the self-supervised KITTI benchmark.
Additionally, SPIdepth achieves the lowest AbsRel (0.029), SqRel (0.069), and
RMSE (1.394) on KITTI, establishing new state-of-the-art results. On
Cityscapes, SPIdepth shows improvements over SQLdepth of 21.7% in AbsRel, 36.8%
in SqRel, and 16.5% in RMSE, even without using motion masks. On Make3D,
SPIdepth in zero-shot outperforms all other models. Remarkably, SPIdepth
achieves these results using only a single image for inference, surpassing even
methods that utilize video sequences for inference, thus demonstrating its
efficacy and efficiency in real-world applications. Our approach represents a
significant leap forward in self-supervised monocular depth estimation,
underscoring the importance of strengthening pose information for advancing
scene understanding in real-world applications.
