Abstract
Monocular depth estimation and ego-motion estimation are significant tasks for scene perception and navigation in stable, accurate and efficient robot-assisted endoscopy. To tackle lighting variations and sparse textures in endoscopic scenes, multiple techniques including optical flow, appearance flow and intrinsic image decomposition have been introduced into the existing methods. However, the effective training strategy for multiple modules are still critical to deal with both illumination issues and information interference for self-supervised depth estimation in endoscopy. Therefore, a novel framework with multistep efficient finetuning is proposed in this work. In each epoch of end-to-end training, the process is divided into three steps, including optical flow registration, multiscale image decomposition and multiple transformation alignments. At each step, only the related networks are trained without interference of irrelevant information. Based on parameter-efficient finetuning on the foundation model, the proposed method achieves state-of-the-art performance on self-supervised depth estimation on SCARED dataset and zero-shot depth estimation on Hamlyn dataset, with 4\%$\sim$10\% lower error. The evaluation code of this work has been published on this https URL.
Abstract (translated)
单目深度估计和自运动估计对于稳定、准确且高效的机器人辅助内窥镜感知与导航任务至关重要。为了应对内窥镜场景中的光照变化和纹理稀疏问题，现有的方法已经引入了包括光流（optical flow）、外观流（appearance flow）和内在图像分解（intrinsic image decomposition）在内的多种技术。然而，对于自监督深度估计而言，在处理照明问题与信息干扰的同时，有效的多模块训练策略仍然至关重要。因此，本文提出了一种具有多步骤高效微调的新框架。 在端到端训练的每个周期中，过程被分为三个阶段：光流配准（optical flow registration）、多尺度图像分解（multiscale image decomposition）和多重变换对齐（multiple transformation alignments）。在每一阶段中，只有相关的网络会被训练，并且不会受到无关信息的干扰。基于基础模型上的参数高效微调方法，该方法在SCARED数据集上实现了自监督深度估计领域的最先进性能，在Hamlyn数据集上也实现了零样本深度估计方面的领先表现，误差降低了4%到10%。 本文的工作代码已发布在此[URL]（请将方括号内的文本替换为实际的网址链接）。 这种描述提供了对所提出方法技术细节和优势的一个清晰概览，并强调了其在特定数据集上的性能提升。
URL
https://arxiv.org/abs/2506.16017
