Abstract
Occlusion and the scarcity of labeled surgical data are significant challenges in disparity estimation for stereo laparoscopic images. To address these issues, this study proposes a Depth Guided Occlusion-Aware Disparity Refinement Network (DGORNet), which refines disparity maps by leveraging monocular depth information unaffected by occlusion. A Position Embedding (PE) module is introduced to provide explicit spatial context, enhancing the network's ability to localize and refine features. Furthermore, we introduce an Optical Flow Difference Loss (OFDLoss) for unlabeled data, leveraging temporal continuity across video frames to improve robustness in dynamic surgical scenes. Experiments on the SCARED dataset demonstrate that DGORNet outperforms state-of-the-art methods in terms of End-Point Error (EPE) and Root Mean Squared Error (RMSE), particularly in occlusion and texture-less regions. Ablation studies confirm the contributions of the Position Embedding and Optical Flow Difference Loss, highlighting their roles in improving spatial and temporal consistency. These results underscore DGORNet's effectiveness in enhancing disparity estimation for laparoscopic surgery, offering a practical solution to challenges in disparity estimation and data limitations.
Abstract (translated)
遮挡和标记的手术数据稀缺是立体腹腔镜图像视差估计中的主要挑战。为解决这些问题，本研究提出了一种深度引导的遮挡感知视差精炼网络（DGORNet），该网络通过利用不受遮挡影响的单目深度信息来精炼视差图。引入了位置嵌入（PE）模块以提供显式的空间上下文，从而增强网络定位和精炼特征的能力。此外，我们提出了一种用于未标记数据的光流差异损失（OFDLoss），通过利用视频帧之间的时序连续性，在动态手术场景中提高鲁棒性。 在SCARED数据集上的实验表明，DGORNet在视差估计的端点误差（EPE）和均方根误差（RMSE）方面优于现有方法，特别是在遮挡区域和纹理缺乏的区域。消融研究证实了位置嵌入和光流差异损失对提高空间一致性和时间一致性的作用。 这些结果强调了DGORNet在提升腹腔镜手术视差估计效果方面的有效性，并为解决视差估计中的挑战及数据限制问题提供了一种实用解决方案。
URL
https://arxiv.org/abs/2505.08178
