arXiv 2602.171822026-02-19cs.CVcs.RO

NRGS-SLAM：通过变形感知的3D高斯点云实现内窥镜单目非刚性SLAM

NRGS-SLAM: Monocular Non-Rigid SLAM for Endoscopy via Deformation-Aware 3D Gaussian Splatting

Department of Mechanical and Automation Engineering and T Stone Robotics Institute, The Chinese University of Hong Kong（机械与自动化工程系和T Stone机器人研究所，香港中文大学）；School of Automation and Intelligent Sensing, the State Key Laboratory of Avionics Integration and Aviation System of-Systems Synthesis, and Shanghai Key Laboratory of Navigation and Location Based Services, Shanghai Jiao Tong University（自动化与智能感知学院、航空系统集成与航空系统综合国家重点实验室、上海导航与定位服务重点实验室，上海交通大学）

Jiwei Shan, Zeyu Cai, Yirui Li, Yongbo Chen, Lijun Han, Yun-hui Liu, Hesheng Wang, Shing Shin Cheng

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AI总结：

NRGS-SLAM通过变形感知的3D高斯点云实现内窥镜单目非刚性SLAM，有效解决刚性假设与变形耦合问题，提升定位精度与重建质量。

摘要：

视觉同时定位与建图（V-SLAM）是自主感知和导航的基本能力。然而，内窥镜场景由于持续的软组织变形违反了刚性假设，导致相机自身运动与内在变形之间存在强耦合模糊性。尽管最近的单目非刚性SLAM方法取得了显著进展，但它们通常缺乏有效的解耦机制，并依赖于稀疏或低保真度的场景表示，这导致跟踪漂移和重建质量有限。为了解决这些限制，我们提出了NRGS-SLAM，一种基于3D高斯点云的内窥镜单目非刚性SLAM系统。为了解决耦合模糊性，我们引入了变形感知的3D高斯地图，该地图通过在每个高斯原始体上附加可学习的变形概率来增强，通过贝叶斯自监督策略优化，而无需外部非刚性标签。在此表示基础上，我们设计了一个可变形跟踪模块，通过优先考虑低变形区域进行鲁棒的粗到细姿态估计，随后进行高效的每帧变形更新。一个精心设计的可变形映射模块逐步扩展和细化地图，平衡了表示能力和计算效率。此外，一个统一的鲁棒几何损失结合了外部几何先验，以缓解单目非刚性SLAM的固有不明确性。在多个公开内窥镜数据集上的广泛实验表明，NRGS-SLAM在相机姿态估计的准确性（RMSE减少高达50%）和高质量的逼真重建方面优于现有最先进方法。全面的消融研究进一步验证了我们关键设计选择的有效性。源代码将在论文接受后公开。

英文摘要：

Visual simultaneous localization and mapping (V-SLAM) is a fundamental capability for autonomous perception and navigation. However, endoscopic scenes violate the rigidity assumption due to persistent soft-tissue deformations, creating a strong coupling ambiguity between camera ego-motion and intrinsic deformation. Although recent monocular non-rigid SLAM methods have made notable progress, they often lack effective decoupling mechanisms and rely on sparse or low-fidelity scene representations, which leads to tracking drift and limited reconstruction quality. To address these limitations, we propose NRGS-SLAM, a monocular non-rigid SLAM system for endoscopy based on 3D Gaussian Splatting. To resolve the coupling ambiguity, we introduce a deformation-aware 3D Gaussian map that augments each Gaussian primitive with a learnable deformation probability, optimized via a Bayesian self-supervision strategy without requiring external non-rigidity labels. Building on this representation, we design a deformable tracking module that performs robust coarse-to-fine pose estimation by prioritizing low-deformation regions, followed by efficient per-frame deformation updates. A carefully designed deformable mapping module progressively expands and refines the map, balancing representational capacity and computational efficiency. In addition, a unified robust geometric loss incorporates external geometric priors to mitigate the inherent ill-posedness of monocular non-rigid SLAM. Extensive experiments on multiple public endoscopic datasets demonstrate that NRGS-SLAM achieves more accurate camera pose estimation (up to 50\% reduction in RMSE) and higher-quality photo-realistic reconstructions than state-of-the-art methods. Comprehensive ablation studies further validate the effectiveness of our key design choices. Source code will be publicly available upon paper acceptance.

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