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.

翻译：自监督单目深度估计因其在自动驾驶和机器人领域的应用而受到广泛关注。尽管现有方法在利用自查询层（SQL）等技术从运动中推断深度方面取得了进展，但它们往往忽视了增强位姿信息的潜力。本文提出SPIdepth，这是一种通过优先增强位姿网络以改进深度估计的新方法。在自查询层奠定的基础上，SPIdepth强调位姿信息在捕捉细粒度场景结构中的重要性。通过提升位姿网络的性能，SPIdepth在场景理解和深度估计方面取得了显著进展。在KITTI、Cityscapes和Make3D等基准数据集上的实验结果表明，SPIdepth实现了最先进的性能，显著超越了先前的方法。具体而言，SPIdepth在自监督KITTI基准测试中位居榜首。此外，SPIdepth在KITTI数据集上取得了最低的AbsRel（0.029）、SqRel（0.069）和RMSE（1.394）指标，创造了新的最优记录。在Cityscapes数据集上，即使不使用运动掩码，SPIdepth在AbsRel、SqRel和RMSE指标上分别比SQLdepth提升了21.7%、36.8%和16.5%。在Make3D数据集上，SPIdepth在零样本设置下的表现优于所有其他模型。值得注意的是，SPIdepth仅使用单张图像进行推理就取得了这些成果，其性能甚至超过了依赖视频序列进行推理的方法，这证明了其在现实应用中的有效性和高效性。我们的方法代表了自监督单目深度估计领域的重大进展，凸显了增强位姿信息对于推动现实场景理解的重要性。