Improved Self-supervised Monocular Endoscopic Depth Estimation based on Pose Alignment-friendly Dynamic View Selection

3D depth estimation in monocular endoscopic videos typically relies on self-supervised learning due to the absence of in-body ground-truth depth labels. This learning paradigm optimizes predicted 3D depth using adjacent frames by aligning camera poses and minimizing photometric discrepancies. However, previous efforts often suffer from insufficient visual cues caused by minimal camera movement between adjacent frames, resulting in significant pose alignment errors and reduced learning effectiveness. In this paper, we propose DVSMono, a dynamic view selection-based self-supervised monocular depth estimation that, for the first time, leverages pose alignment-friendly views instead of static adjacent ones. DVSMono employs a pretrained pose estimator to calculate poses between the target frame and its surrounding frames over a larger time span. Accurate poses can create highly-unanimous geometric cost volumes across different depth proposals, while errors often appear as outliers. Motivated by this, we design Temporally-Consistent View Scoring (TCVS), which selects candidate frames with cost volumes showing minimal temporal variance. Additionally, we introduce Photometric-Valid Region Computation (PVRC), which filters out unreliable regions between frames by identifying non-overlapping fields of view and bidirectionally inconsistent occlusion pixels. Ultimately, among the TCVS-selected candidates, DVSMono assigns each target frame its most suitable source frame with the largest PVRC-filtered valid regions. This enables the training of a robust depth estimator, benefiting from improved self-supervised learning built upon dynamically-selected, pose alignment-friendly source-target pairs. Benchmark results on three public datasets demonstrate that DVSMono exceeds the existing state-of-the-art methods by reducing Abs Rel by at least 6.35%. Codes are at https://github.com/adam99goat/DVSMono.