Monocular depth estimation is critical for endoscopists to perform spatial
perception and 3D navigation of surgical sites. However, most of the existing
methods ignore the important geometric structural consistency, which inevitably
leads to performance degradation and distortion of 3D reconstruction. To
address this issue, we introduce a gradient loss to penalize edge fluctuations
ambiguous around stepped edge structures and a normal loss to explicitly
express the sensitivity to frequently small structures, and propose a geometric
consistency loss to spreads the spatial information across the sample grids to
constrain the global geometric anatomy structures. In addition, we develop a
synthetic RGB-Depth dataset that captures the anatomical structures under
reflections and illumination variations. The proposed method is extensively
validated across different datasets and clinical images and achieves mean RMSE
values of 0.066 (stomach), 0.029 (small intestine), and 0.139 (colon) on the
EndoSLAM dataset. The generalizability of the proposed method achieves mean
RMSE values of 12.604 (T1-L1), 9.930 (T2-L2), and 13.893 (T3-L3) on the
ColonDepth dataset. The experimental results show that our method exceeds
previous state-of-the-art competitors and generates more consistent depth maps
and reasonable anatomical structures. The quality of intraoperative 3D
structure perception from endoscopic videos of the proposed method meets the
accuracy requirements of video-CT registration algorithms for endoscopic
navigation. The dataset and the source code will be available at
this https URL

本文提出了一种在内窥镜术中进行单目深度估计的方法，该方法利用几何一致性损失和梯度损失来提高重建效果，并使用合成的 RGB-Depth 数据集来捕捉解剖结构上的反射和照明变化，实验结果表明该方法在实现视频 - CT 配准算法的精度要求时，具有优于现有方法的表现。