Bridging natural language and 3D geometry is a crucial step toward flexible, language-driven scene understanding. While recent advances in 3D Gaussian Splatting (3DGS) have enabled fast and high-quality scene reconstruction, research has also explored incorporating open-vocabulary understanding into 3DGS. However, most existing methods require iterative optimization over per-view 2D semantic feature maps, which not only results in inefficiencies but also leads to inconsistent 3D semantics across views. To address these limitations, we introduce a training-free framework that constructs a superpoint graph directly from Gaussian primitives. The superpoint graph partitions the scene into spatially compact and semantically coherent regions, forming view-consistent 3D entities and providing a structured foundation for open-vocabulary understanding. Based on the graph structure, we design an efficient reprojection strategy that lifts 2D semantic features onto the superpoints, avoiding costly multi-view iterative training. The resulting representation ensures strong 3D semantic coherence and naturally supports hierarchical understanding, enabling both coarse- and fine-grained open-vocabulary perception within a unified semantic field. Extensive experiments demonstrate that our method achieves state-of-the-art open-vocabulary segmentation performance, with semantic field reconstruction completed over $30\times$ faster. Our code will be available at https://github.com/Atrovast/THGS.
Nanjing University of Science and Technology（南京理工大学）
Point cloud semantic segmentation is a crucial task in 3D scene understanding. Existing methods mainly focus on employing a large number of annotated labels for supervised semantic segmentation. Nonetheless, manually labeling such large point clouds for the supervised segmentation task is time-consuming. In order to reduce the number of annotated labels, we propose a semi-supervised semantic point cloud segmentation network, named SSPC-Net, where we train the semantic segmentation network by inferring the labels of unlabeled points from the few annotated 3D points. In our method, we first part…
CSS: Overcoming Pose and Scene Challenges in Crowd-Sourced 3D Gaussian Splatting
Beijing University of Posts and Telecommunications（北京邮电大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；China Unicom Smart City Research Institute（中国联通智慧城市研究院）
3DitScene: Editing Any Scene via Language-guided Disentangled Gaussian Splatting
The Chinese University of Hong Kong（香港中文大学）；Stanford University（斯坦福大学）；Snap Inc.（Snap公司）；University of California Los Angeles（加利福尼亚大学洛杉矶分校）；ByteDance（字节跳动）
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）
Holistic Large-Scale Scene Reconstruction via Mixed Gaussian Splatting
School of Computer Science, Wuhan University（武汉大学计算机学院）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）；School of Electronic Information, Wuhan University（武汉大学电子信息学院）；State Key Lab. of LIESMARS, Wuhan University（武汉大学LIESMARS国家重点实验室）；Institute for Math & AI, Wuhan University（武汉大学数学与人工智能研究院）
SceneSplat++: A Large Dataset and Comprehensive Benchmark for Language Gaussian Splatting
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；ETH Zürich（苏黎世联邦理工学院）；University of Amsterdam（阿姆斯特丹大学）；Johns Hopkins University（约翰·霍普金斯大学）；University of Pisa（比萨大学）；University of Trento（特伦托大学）
Horizon-GS: Unified 3D Gaussian Splatting for Large-Scale Aerial-to-Ground Scenes
University of Science and Technology of China（中国科学技术大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；The Chinese University of Hong Kong（香港中文大学）；Brown University（布朗大学）；The University of Hong Kong（香港大学）
DreamScape: 3D Scene Creation via Gaussian Splatting joint Correlation Modeling
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；School of Artificial Intelligence, Beihang University（北京航空航天大学人工智能学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Seeing Through Reflections: Advancing 3D Scene Reconstruction in Mirror-Containing Environments with Gaussian Splatting
School of Automation and Intelligent Sening, Shanghai Jiao Tong University（自动化与智能传感学院，上海交通大学）；Ningbo Artificial Intelligence Institute, Shanghai Jiao Tong University（宁波人工智能研究所，上海交通大学）
Underwater360: Reconstructing Underwater Scenes from Panoramic Images with Omnidirectional Gaussian Splatting
School of Software, Dalian University of Technology（大连理工大学软件学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
