SVT-SDE: Spatiotemporal Vision Transformers-Based Self-Supervised Depth Estimation in Stereoscopic Surgical Videos

Dense depth estimation plays a crucial role in developing context-aware computer-assisted intervention systems. However, it is a challenging task due to low image quality and highly dynamic surgical environment. The task is further complicated by the difficulty in acquiring per-pixel ground truth depth data in a surgical setting. Recent works on self-supervised depth estimation use image reconstruction (i.e., the warped images) as supervisory signal, which helps to eliminate the requirement of ground truth depth annotations but also causes over-smoothed depth predictions. Additionally, most existing depth estimation methods are built upon static laparoscopic images, ignoring rich temporal information. To address these challenges, we propose a novel spatiotemporal vision transformers-based self-supervised depth estimation method, referred as SVT-SDE. Unlike previous works, SVT-SDE features a novel spatiotemporal vision transformers (SVT) architecture, which can learn complementary visual and temporal information from the input stereoscopic video clips. We further introduce high-frequency-based supervisory signal, which helps to preserve fine-grained details of depth estimation. Results from experiments conducted on two publicly available datasets demonstrate the superior performance of SVT-SDE over the state-of-the-art self-supervised depth estimation methods.