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 and Cityscapes showcase SPIdepth's
state-of-the-art performance, surpassing previous methods by significant
margins. Notably, SPIdepth's performance exceeds that of unsupervised models
and, after finetuning on metric data, outperforms all existing methods.
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.

自监督单眼深度估计 (SPIdepth) 是一种在自动驾驶和机器人领域应用广泛的方法，通过加强姿势网络提升深度估计的能力，在捕捉细粒度场景结构方面取得了显著进展，通过在 KITTI 和 Cityscapes 等基准数据集上的实验证明了该方法的卓越性能，超越了现有方法。尤其值得注意的是，SPIdepth 的性能超越了无监督模型，并且在度量数据的微调后超过了现有方法，仅通过一张图像进行推断，超越了利用视频序列进行推断的方法，充分展示了该方法在实际应用中的有效性和效率。本研究对于自监督单眼深度估计具有重要的意义，强调了加强姿势信息在推进实际应用中的场景理解方面的重要性。