Depth estimation is a cornerstone of 3D reconstruction and plays a vital role in minimally invasive endoscopic surgeries. However, most current depth estimation networks rely on traditional convolutional neural networks, which are limited in their ability to capture global information. Foundation models offer a promising avenue for enhancing depth estimation, but those currently available are primarily trained on natural images, leading to suboptimal performance when applied to endoscopic images. In this work, we introduce a novel fine-tuning strategy for the Depth Anything Model and integrate it with an intrinsic-based unsupervised monocular depth estimation framework. Our approach includes a low-rank adaptation technique based on random vectors, which improves the model's adaptability to different scales. Additionally, we propose a residual block built on depthwise separable convolution to compensate for the transformer's limited ability to capture high-frequency details, such as edges and textures. Our experimental results on the SCARED dataset show that our method achieves state-of-the-art performance while minimizing the number of trainable parameters. Applying this method in minimally invasive endoscopic surgery could significantly enhance both the precision and safety of these procedures.

本研究解决了现有深度估计网络在内窥镜图像应用中的局限性。我们提出了一种新颖的微调策略，结合内在的无监督单目深度估计框架，利用随机向量的低秩适应技术，提高模型对不同尺度的适应能力。实验结果表明，该方法在精度和安全性上显著增强了微创内窥镜手术的效果。

推进深度任何模型以无监督的单目深度估计在内窥镜中的应用