GSM-GS: Geometry-Constrained Single and Multi-view Gaussian Splatting for Surface Reconstruction
School of Automation and Intelligent Manufacturing, Southern University Science and Technology（自动化与智能制造学院，南方科技大学）；Research Institute of Mine Artificial Intelligence, China Coal Research Institute（煤矿人工智能研究院，中国煤炭研究总院）；Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（人工智能与机器人研究院，西安交通大学）
Xiao Ren, Yu Liu, Ning An, Jian Cheng, Xin Qiao, He Kong
Recently, 3D Gaussian Splatting has emerged as a prominent research direction owing to its ultrarapid training speed and high-fidelity rendering capabilities. However, the unstructured and irregular nature of Gaussian point clouds poses challenges to reconstruction accuracy. This limitation frequently causes high-frequency detail loss in complex surface microstructures when relying solely on routine strategies. To address this limitation, we propose GSM-GS: a synergistic optimization framework integrating single-view adaptive sub-region weighting constraints and multi-view spatial structure refinement. For single-view optimization, we leverage image gradient features to partition scenes into texture-rich and texture-less sub-regions. The reconstruction quality is enhanced through adaptive filtering mechanisms guided by depth discrepancy features. This preserves high-weight regions while implementing a dual-branch constraint strategy tailored to regional texture variations, thereby improving geometric detail characterization. For multi-view optimization, we introduce a geometry-guided cross-view point cloud association method combined with a dynamic weight sampling strategy. This constructs 3D structural normal constraints across adjacent point cloud frames, effectively reinforcing multi-view consistency and reconstruction fidelity. Extensive experiments on public datasets demonstrate that our method achieves both competitive rendering quality and geometric reconstruction. See our interactive project page
GeoSplat: A Deep Dive into Geometry-Constrained Gaussian Splatting
Department of Applied Mathematics and Theoretical Physics, University of Cambridge（应用数学与理论物理系，剑桥大学）；Institut Camille Jordan, Université Claude Bernard Lyon 1（让·Camille Jordan研究所，克莱蒙费朗斯大学）
UGOD: Uncertainty-Guided Opacity and Dropout for Sparse-View 3D Gaussian Splatting
Manchester Metropolitan University（曼彻斯特城市大学）；University of Surrey（萨里大学）；Imperial College London（帝国理工学院）；University of Exeter（埃克塞特大学）；Southeast University（东南大学）；Nanjing University of Science and Technology（南京理工大学）；University of Leeds（利兹大学）；Aston University（阿斯顿大学）
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））
Unfolding 3D Gaussian Splatting via Iterative Gaussian Synopsis
School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）；School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算与信息系统学院）
DAV-GSWT: Diffusion-Active-View Sampling for Data-Efficient Gaussian Splatting Wang Tiles
University of Macau（澳门大学）；Juntendo University（立命馆大学）；Tongji University（同济大学）；Renmin University of China（中国人民大学）；University of Chinese Academy of Sciences（中国科学院大学）；Zhejiang University（浙江大学）；University of Liverpool（利物浦大学）
Multi-Layer Gaussian Splatting for Immersive Anatomy Visualization
Technical University of Munich（慕尼黑技术大学）；Clinic for Orthopedics and Sports Orthopedics（骨科与运动骨科诊所）；TUM University Hospital（慕尼黑技术大学医院）；Siemens Healthineers AG（西门子医疗影像有限公司）
GaussianFlow: Splatting Gaussian Dynamics for 4D Content Creation
University of Southern California（南加州大学）；Google（谷歌）；Pennsylvania State University（宾夕法尼亚州立大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
InterGSEdit: Interactive 3D Gaussian Splatting Editing with 3D Geometry-Consistent Attention Prior
MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（信息产业部模式分析与机器智能重点实验室，计算机科学与技术学院，南京航空航天大学）；The Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology（教育部高维信息智能感知与系统重点实验室，计算机科学与工程学院，南京理工大学）
MEGA: Memory-Efficient 4D Gaussian Splatting for Dynamic Scenes
iComAI Lab, The Hong Kong University of Science and Technology（香港科技大学iComAI实验室）；Skywork AI；The Chinese University of Hong Kong（香港中文大学）；National University of Singapore（新加坡国立大学）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）
Nix and Fix: Targeting 1000x Compression of 3D Gaussian Splatting with Diffusion Models
1 Chair of Media Technology, Munich Institute of Robotics；Machine Intelligence School of Computation, Information；Technology Technical University of Munich, 80333 Munich, Germany 2LTCI, T\'el\'ecom Paris, Institut Polytechnique de Paris, France
LODGE: Level-of-Detail Large-Scale Gaussian Splatting with Efficient Rendering
Google（谷歌）；Google DeepMind（谷歌DeepMind）；Technical University of Munich（慕尼黑技术大学）；Czech Technical University in Prague（布拉格捷克技术大学）；Czech Institute of Informatics, Robotics and Cybernetics（捷克信息学、机器人学与自动化研究所）
DynActiveGS: Active Gaussian Splatting for Dynamic Scene Reconstruction
Tsinghua University, Shenzhen International Graduate School（清华大学深圳国际研究生院）；Huawei Technologies Ltd（华为技术有限公司）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Peng Cheng Laboratory（鹏城实验室）
