arXiv 2506.214202025-07-05cs.CVcs.RO

EndoFlow-SLAM：基于流约束高斯泼溅的实时内窥镜SLAM

EndoFlow-SLAM: Real-Time Endoscopic SLAM with Flow-Constrained Gaussian Splatting

School of Advanced Technology, Xi'an Jiaotong Liverpool University（西安交通大学利物浦大学先进技术学院）；School of AI and Advanced Computing, Xi'an Jiaotong Liverpool University（西安交通大学利物浦大学人工智能与高级计算学院）；School of Physical Sciences, University of Liverpool（利物浦大学物理科学学院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））

Taoyu Wu, Yiyi Miao, Zhuoxiao Li, Haocheng Zhao, Kang Dang, Jionglong Su, Limin Yu, Haoang Li

备注：: This paper has been accepted at MICCAI2025

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

本文提出EndoFlow-SLAM，通过引入光流损失和深度正则化策略，并改进3DGS细化策略，解决了内窥镜场景中光度不一致和动态运动问题，在静态和动态手术场景中均优于现有方法。

摘要：

高效的三维重建与实时可视化在内窥镜等手术场景中至关重要。近年来，三维高斯泼溅（3DGS）在高效三维重建与渲染方面展现出卓越性能。大多数基于3DGS的同步定位与建图（SLAM）方法仅依赖外观约束来同时优化3DGS和相机位姿。然而，在内窥镜场景中，非朗伯表面引起的光度不一致性以及呼吸引起的动态运动影响了SLAM系统的性能。为解决这些问题，我们额外引入光流损失作为几何约束，有效约束场景的三维结构和相机运动。此外，我们提出一种深度正则化策略，以缓解光度不一致性问题，并确保内窥镜场景中3DGS深度渲染的有效性。另外，为改善SLAM系统中的场景表示，我们改进了3DGS细化策略，重点关注与渲染质量欠佳的关键帧相对应的视点，从而获得更好的渲染结果。在C3VD静态数据集和StereoMIS动态数据集上的大量实验表明，我们的方法在新视角合成和位姿估计方面优于现有最先进方法，在静态和动态手术场景中均展现出高性能。

英文摘要：

Efficient three-dimensional reconstruction and real-time visualization are critical in surgical scenarios such as endoscopy. In recent years, 3D Gaussian Splatting (3DGS) has demonstrated remarkable performance in efficient 3D reconstruction and rendering. Most 3DGS-based Simultaneous Localization and Mapping (SLAM) methods only rely on the appearance constraints for optimizing both 3DGS and camera poses. However, in endoscopic scenarios, the challenges include photometric inconsistencies caused by non-Lambertian surfaces and dynamic motion from breathing affects the performance of SLAM systems. To address these issues, we additionally introduce optical flow loss as a geometric constraint, which effectively constrains both the 3D structure of the scene and the camera motion. Furthermore, we propose a depth regularisation strategy to mitigate the problem of photometric inconsistencies and ensure the validity of 3DGS depth rendering in endoscopic scenes. In addition, to improve scene representation in the SLAM system, we improve the 3DGS refinement strategy by focusing on viewpoints corresponding to Keyframes with suboptimal rendering quality frames, achieving better rendering results. Extensive experiments on the C3VD static dataset and the StereoMIS dynamic dataset demonstrate that our method outperforms existing state-of-the-art methods in novel view synthesis and pose estimation, exhibiting high performance in both static and dynamic surgical scenes.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。