Adaptation methods are developed to adapt depth foundation models to endoscopic depth estimation recently. However, such approaches typically under-perform training since they limit the parameter search to a low-rank subspace and alter the training dynamics. Therefore, we propose a full-parameter and parameter-efficient learning framework for endoscopic depth estimation. At the first stage, the subspace of attention, convolution and multi-layer perception are adapted simultaneously within different sub-spaces. At the second stage, a memory-efficient optimization is proposed for subspace composition and the performance is further improved in the united sub-space. Initial experiments on the SCARED dataset demonstrate that results at the first stage improves the performance from 10.2% to 4.1% for Sq Rel, Abs Rel, RMSE and RMSE log in the comparison with the state-of-the-art models.

本研究解决了当前内窥镜深度估计领域中参数适应方法的不足，即限制了参数搜索并影响了训练动态。我们提出了一种全参数与高效参数的学习框架，能够同时适应多种子空间，显著提高了性能，初步实验表明在SCARED数据集上，该方法的性能改进幅度达到10.2%到4.1%。

面向全参数与高效参数自学习的内窥镜相机深度估计