Gaussian splatting is the current state-of-the-art for dense, deformable 3D anatomy reconstruction in robot-assisted minimally invasive surgery (RAMIS); however, most pipelines are offline and depend on accurate camera trajectory priors (often from robotic kinematics), limiting applicability when priors are missing or noisy. To address these limitations, we propose Track2Map, an online 3D Gaussian Splatting pipeline that jointly optimizes camera trajectory and 3D deformable scene representation directly from surgical video.
The paper presents a camera‑pose‑free stereo endoscopic method for recovering tissue deformation by modeling geometry as a 3D point‑derivative map and deformation as a 3D displacement‑local deformation map. It optimizes inter‑frame deformation in a camera‑centric setting, eliminating the need for camera pose estimation, and introduces a canonical map for online geometry and deformation optimization. Experiments on in‑vivo and ex‑vivo laparoscopic data show accurate 3D reconstruction (≈0.37–0.39 mm surface distance) even under occlusion, and the method can estimate surface strain distributions during manipulation.
By Jiahe Chen, Naoki Tomii, Ichiro Sakuma, Etsuko Kobayashi
arXiv:2609.14313v1 Announce Type: cross
Abstract: Robust point tracking in endoscopic videos is essential for computer-assisted intervention and autonomous robotic surgery, enabling continuous regist...
By Jiaming Zhang, Zijian Wu, Mehran Armand, Septimiu Salcudean
LapaTrack-3D is a real‑time 6 DoF tracking algorithm designed for monocular laparoscopic surgery, aligning intra‑operative video with pre‑operative CT data. It modifies the ORB‑SLAM2 framework with four key changes: fast initialization using a primitive 3D shape, a pseudo‑segmentation strategy to isolate the target organ, incorporation of the 3D shape as a geometric prior in pose graph optimization, and enhanced imaging via a modified Multi‑Scale Retinex with Chromaticity Preservation algorithm. Experiments on in‑vivo and ex‑vivo data show robust tracking under challenging conditions such as poor illumination, fast motion, and partial visibility, achieving 13 Hz on 1280×720 video.
By Jingwei Song, Javid Hussain Jakir, Ray Zhang, Wenwei Zhang, Hao Zhou, Xiaomeng Xian, Maani Ghaffari
Existing Gaussian-splatting-based monocular Simultaneous Localization and Mapping (SLAM) systems are either tailored to short sequences, are not real-time, or suffer from prohibitive GPU memory requirements, limiting their applicability in realistic, long-horizon scenarios. To address this, we present GLAM-SLAM, a real-time, decoupled Gaussian-splatting SLAM system designed for large-scale outdoor scenes.
arXiv:2609.17387v1 Announce Type: new
Abstract: Real-time dense SLAM is a core capability for robotics applications that require robust localization and high- quality mapping in dynamic or fast-chang...
By Yongqi Mao, Hao Shi, Yufan Zhang, Zhonghua Yi, Xiangfei Guo, Kaiwei Wang