提出Segment then Splat，一种基于高斯喷溅的三维开放词汇分割方法，通过先分割后重建实现静态与动态场景的物体级分割，并利用CLIP嵌入支持开放词汇查询。
摘要：
三维空间中的开放词汇查询对于在机器人、自主系统和增强现实等应用中实现更智能的感知至关重要。然而，大多数现有方法依赖于二维像素级解析，导致多视图不一致和三维物体检索效果不佳。此外，它们仅限于静态场景，由于运动建模的复杂性，难以处理动态场景。在本文中，我们提出了Segment then Splat，一种基于高斯喷溅的、适用于静态和动态场景的三维感知开放词汇分割方法。Segment then Splat颠覆了长期以来的“先重建后分割”范式，在重建之前将高斯体划分为不同的物体集合。一旦重建完成，场景便自然地分割为各个物体，实现了真正的三维分割。这种设计消除了几何和语义上的歧义，以及动态场景中高斯体与物体错位的问题。它还加速了优化过程，因为无需学习单独的语言场。优化后，为每个物体分配一个CLIP嵌入，以实现开放词汇查询。在多个数据集上进行的大量实验证明了我们提出的方法在静态和动态场景中的有效性。
英文摘要：
Open-vocabulary querying in 3D space is crucial for enabling more intelligent perception in applications such as robotics, autonomous systems, and augmented reality. However, most existing methods rely on 2D pixel-level parsing, leading to multi-view inconsistencies and poor 3D object retrieval. Moreover, they are limited to static scenes and struggle with dynamic scenes due to the complexities of motion modeling. In this paper, we propose Segment then Splat, a 3D-aware open vocabulary segmentation approach for both static and dynamic scenes based on Gaussian Splatting. Segment then Splat reverses the long established approach of "segmentation after reconstruction" by dividing Gaussians into distinct object sets before reconstruction. Once reconstruction is complete, the scene is naturally segmented into individual objects, achieving true 3D segmentation. This design eliminates both geometric and semantic ambiguities, as well as Gaussian-object misalignment issues in dynamic scenes. It also accelerates the optimization process, as it eliminates the need for learning a separate language field. After optimization, a CLIP embedding is assigned to each object to enable open-vocabulary querying. Extensive experiments one various datasets demonstrate the effectiveness of our proposed method in both static and dynamic scenarios.
University of Liverpool（利物浦大学）；Xi’an Jiaotong-Liverpool University（西交利物浦大学）；Universidad Antonio de Nebrija（安东尼奥·德·内布里哈大学）；ARIES Research Centre（ARIES研究中心）
LLGS: Unsupervised Gaussian Splatting for Image Enhancement and Reconstruction in Pure Dark Environment
School of Engineering and Informatics, University of Sussex（萨塞克斯大学工程与信息学院）；University of Electronic Science and Technology of China（电子科技大学）；Shanghai Jiao Tong University（上海交通大学）；Dalian University of Technology（大连理工大学）
A Mixed-Primitive-based Gaussian Splatting Method for Surface Reconstruction
Lancaster University, United Kingdom（兰卡斯特大学，英国）；The University of Queensland, Australia（昆士兰大学，澳大利亚）；The Hong Kong Polytechnic University, China（香港理工大学，中国）；University at Buffalo, United States of America（布法罗大学，美国）
Analytic-Splatting: Anti-Aliased 3D Gaussian Splatting via Analytic Integration
South China University of Technology（华南理工大学）；Tencent AI Lab（腾讯AI实验室）；City University of Hong Kong（香港城市大学）；School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）
SA-GS: Scale-Adaptive Gaussian Splatting for Training-Free Anti-Aliasing
Tongji University（同济大学）；Ocean University of China（中国海洋大学）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）；Duke Kunshan University（昆山杜克大学）
PEGASUS: Physically Enhanced Gaussian Splatting Simulation System for 6DoF Object Pose Dataset Generation
Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大-埃尔兰根-纽伦堡大学）；National Institute of Advanced Industrial Science and Technology（日本先进工业科学技术研究院）；Industrial CPS Research Center（工业网络物理系统研究中心）
GSurf: Learning Signed Distance Fields from Splatting Opaque Gaussians for High-quality 3D Reconstruction
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；School of Software, Dalian University of Technology（大连理工大学软件学院）
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院，清华大学）；Institute for AI Industry Research (AIR), Tsinghua University（人工智能产业研究院（AIR），清华大学）；Beijing Academy of Artificial Intelligence, BAAI（北京人工智能研究院，BAAI）；Fudan University（复旦大学）；Peking University（北京大学）
Fi-Gaussian: Frequency-Aware Implicit Gaussian Splatting for Single Image Dehazing
College of Mechanical and Vehicle Engineering, Chongqing University（重庆大学机械与车辆工程学院）；Department of Electrical and Computer Engineering, Carnegie Mellon University（卡内基梅隆大学电气与计算机工程系）；National Innovation Center of Intelligent and Connected Vehicles（国家智能网联汽车创新中心）；School of Vehicle and Mobility, Tsinghua University（清华大学车辆与运载学院）
