Efficient and high-fidelity reconstruction of deformable surgical scenes is a critical yet challenging task. Building on recent advancements in 3D Gaussian splatting, current methods have seen significant improvements in both reconstruction quality and rendering speed. However, two major limitations remain: (1) difficulty in handling irreversible dynamic changes, such as tissue shearing, which are common in surgical scenes; and (2) the lack of hierarchical modeling for surgical scene deformation, which reduces rendering speed. To address these challenges, we introduce EH-SurGS, an efficient and high-fidelity reconstruction algorithm for deformable surgical scenes. We propose a deformation modeling approach that incorporates the life cycle of 3D Gaussians, effectively capturing both regular and irreversible deformations, thus enhancing reconstruction quality. Additionally, we present an adaptive motion hierarchy strategy that distinguishes between static and deformable regions within the surgical scene. This strategy reduces the number of 3D Gaussians passing through the deformation field, thereby improving rendering speed. Extensive experiments demonstrate that our method surpasses existing state-of-the-art approaches in both reconstruction quality and rendering speed. Ablation studies further validate the effectiveness and necessity of our proposed components. We will open-source our code upon acceptance of the paper.
SurgicalGS: Dynamic 3D Gaussian Splatting for Accurate Robotic-Assisted Surgical Scene Reconstruction
The Hamlyn Centre for Robotic Surgery, Imperial College London（伦敦帝国理工学院哈姆林 robotic外科中心）；Hawkes Institute, University College London（伦敦大学学院霍克斯研究所）；University of Liverpool（利物浦大学）
SurgicalGaussian: Deformable 3D Gaussians for High-Fidelity Surgical Scene Reconstruction
Center for Digital Media Computing（数字媒体计算中心）；School of Film（电影学院）；School of Informatics（信息学院）；National Institute for Data Science in Health and Medicine（健康与医学数据科学国家研究院）；Xiamen University（厦门大学）；Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan, Ministry of Culture and Tourism（文化和旅游部闽台非物质文化遗产数字化保护与智能处理重点实验室）；Guangdong Provincial Key Laboratory IRADS（广东省IRADS重点实验室）；BNU-HKBU United International College（北京师范大学-香港浸会大学联合国际学院）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）
BridgeSplat: Bidirectionally Coupled CT and Non-Rigid Gaussian Splatting for Deformable Intraoperative Surgical Navigation
Computer Aided Medical Procedures, TU Munich, Munich, Germany（慕尼黑技术大学计算机辅助医疗程序部）；Department of General, Visceral, and Transplantation Surgery, Hospital of the LMU Munich, Ludwig-Maximilians-Universität (LMU), Munich, Germany（慕尼黑路德维希-马克西米利安大学（LMU）普通外科、 visceral外科及移植外科部门）；Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA（哈佛医学院布里特妇女医院）
DeGenseGS: Geometrically and Semantically Decoupled Surgical Scene Understanding in 4D Gaussian Splatting
School of Automation, Southeast University（东南大学自动化学院）；School of Internet of Things, Nanjing University of Posts and Telecommunications（南京邮电大学物联网学院）；Department of Biomedical Engineering, National University of Singapore（新加坡国立大学生物医学工程系）
DGNS: Deformable Gaussian Splatting and Dynamic Neural Surface for Monocular Dynamic 3D Reconstruction
Industrial Research Organisation, Canberra, ACT Australia；College of Engineering Computing \& Cybernetics, The Australian National University, Canberra, ACT Australia；Faculty of Engineering, The University of Hong Kong, Hong Kong SAR China；Industrial Research Organisation；College of Engineering Computing \& Cybernetics, The Australian National University；Faculty of Engineering, The University of Hong Kong
SAGS: Self-Adaptive Alias-Free Gaussian Splatting for Dynamic Surgical Endoscopic Reconstruction
University of Technology Sydney, Australia（悉尼技术大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China（深圳先进技术研究所）；Shenyang Institute of Automation, Chinese Academy of Sciences, China（沈阳自动化研究所）
DLO-Splatting: Tracking Deformable Linear Objects Using 3D Gaussian Splatting
Department of Aerospace Engineering and the Coordinated Science Laboratory at the University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校航空航天工程系及协调科学实验室）；Division of Robotics, Perception and Learning at KTH Royal Institute of Technology（皇家理工学院机器人、感知与学习 division）；NASA Ames Research Center（NASA阿姆斯研究中心）
Progressively Deformable 2D Gaussian Splatting for Video Representation at Arbitrary Resolutions
Shanghai Jiao Tong University, Shanghai, China（上海交通大学）；University of Missouri--Kansas City, Kansas City, MO, USA（密苏里大学-堪萨斯城分校）；University of Canterbury, Christchurch, New Zealand（坎特伯雷大学）
SurgTPGS: Semantic 3D Surgical Scene Understanding with Text Promptable Gaussian Splatting
The Chinese University of Hong Kong, Hong Kong SAR, China（香港中文大学）；Shenzhen Research Institute, CUHK, Shenzhen, China（深圳研究学院）；Technical University of Munich, Munich, Germany（慕尼黑技术大学）；University of Strasbourg & IHU Strasbourg, Strasbourg, France（斯特拉斯堡大学及斯特拉斯堡IHU）；University College London, London, United Kingdom（伦敦大学学院）
Rasterizing Wireless Radiance Field via Deformable 2D Gaussian Splatting
School of Information Science and Electronic Engineering, Shanghai Jiao Tong University（信息科学与电子工程学院，上海交通大学）；University of Missouri-Kansas City（密苏里大学-凯斯城分校）；Lenovo Group, Lenovo Research（联想集团，联想研究院）
Reconstruction of the soft tissues in robotic surgery from endoscopic stereo videos is important for many applications such as intra-operative navigation and image-guided robotic surgery automation. Previous works on this task mainly rely on SLAM-based approaches, which struggle to handle complex surgical scenes. Inspired by recent progress in neural rendering, we present a novel framework for deformable tissue reconstruction from binocular captures in robotic surgery under the single-viewpoint setting. Our framework adopts dynamic neural radiance fields to represent deformable surgical scenes…
Free-DyGS: Camera-Pose-Free Scene Reconstruction for Dynamic Surgical Videos with Gaussian Splatting
Department of Biomedical Engineering, National University of Singapore (NUS)（生物医学工程系，新加坡国立大学）；National University of Singapore（新加坡国立大学）；Yong Loo Lin School of Medicine, NUS（Yong Loo Lin医学院，新加坡国立大学）；Department of Surgery, National University Hospital（国立大学医院外科部）；Centre for Smart Health, The Hong Kong Polytechnic University（智能健康中心，香港理工大学）
Endo-4DGS: Endoscopic Monocular Scene Reconstruction with 4D Gaussian Splatting
The Chinese University of Hong Kong (CUHK)（香港中文大学）；University College London（伦敦大学学院）；Shun Hing Institute of Advanced Engineering, CUHK（香港中文大学信兴高等工程研究所）；Shenzhen Research Institute, CUHK（香港中文大学深圳研究院）
Light4GS: Lightweight Compact 4D Gaussian Splatting Generation via Context Model
School of Information Science and Electronic Engineering, Shanghai Jiao Tong University（信息科学与电子工程学院，上海交通大学）；School of Science and Engineering, University of Missouri–Kansas City（科学与工程学院，密苏里大学-堪萨斯城分校）；University of Chinese Academy of Sciences（中国科学院大学）
SurgCalib: Gaussian Splatting-Based Hand-Eye Calibration for Robot-Assisted Minimally Invasive Surgery
Robotics and Control Laboratory, University of British Columbia（机器人与控制实验室，不列颠哥伦比亚大学）；Department of Biomedical Engineering, National University of Singapore（生物医学工程系，新加坡国立大学）；Division of Otolaryngology – Head and Neck Surgery, University of British Columbia（耳鼻喉科–头颈外科部，不列颠哥伦比亚大学）
Towards Patient-Specific Deformable Registration in Laparoscopic Surgery
Biomedical Robotics Lab, Istituto Italiano di Tecnologia, Genoa, Italy（生物医学机器人实验室，意大利理工学院，热那亚）；Department of Computer Science, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genoa, Italy（计算机科学、生物工程、机器人与系统工程系（DIBRIS），热那亚大学，意大利）；Harvard Medical School, Brigham and Women’s Hospital, Boston, USA（哈佛医学院，布里洛妇女医院，美国波士顿）
DEGS: Deformable Event-based 3D Gaussian Splatting from RGB and Event Stream
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Hong Kong University of Science and Technology（香港科技大学）；University of Hong Kong（香港大学）；Northeastern University（东北大学）
4D Monocular Surgical Reconstruction under Arbitrary Camera Motions
Department of Mechanical and Automation Engineering（机械与自动化工程系）；T Stone Robotics Institute, The Chinese University of Hong Kong, Hong Kong（T Stone机器人研究院，香港中文大学，香港）；Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China（沈阳自动化研究所，中国科学院，沈阳，中国）；State Key Laboratory of Robotics and Intelligent Systems, Shenyang, China（机器人与智能系统国家重点实验室，中国，沈阳）；School of Integrated Circuits, Shanghai Jiao Tong University, Shanghai, China（集成电路学院，上海交通大学，中国，上海）；School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China（自动化与智能感知学院，上海交通大学，中国，上海）；Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China（系统控制与信息处理重点实验室，中国教育部，中国，上海）
University of California San Diego（加利福尼亚大学圣地亚哥分校）；Zhejiang University（浙江大学）；Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies（阿里巴巴-浙江大学前沿技术联合研究院）
Traditional control and task automation have been successfully demonstrated in a variety of structured, controlled environments through the use of highly specialized modeled robotic systems in conjunction with multiple sensors. However, the application of autonomy in endoscopic surgery is very challenging, particularly in soft tissue work, due to the lack of high-quality images and the unpredictable, constantly deforming environment. In this work, we propose a novel surgical perception framework, SuPer, for surgical robotic control. This framework continuously collects 3D geometric information…
