3D Gaussian Splatting (3DGS) provides an explicit and efficient scene representation, but its primitives lack inherent object-level identity, hindering downstream tasks such as open-vocabulary scene understanding. Existing methods typically address this by either distilling high-dimensional feature embeddings into Gaussians or by lifting 2D mask labels into 3D via heuristic refinement. However, feature-based approaches incur heavy storage and decoding overhead, while lifting-based pipelines remain vulnerable to label contamination: Gaussians necessary for appearance reconstruction often receive incorrect object labels during 2D-to-3D projection. We propose OP2GS, an object-aware Gaussian representation that augments each primitive with an explicit instance identity and a dedicated instance opacity $σ^{*}$ for object-mask rendering. The original opacity $σ$ remains responsible for visual reconstruction, while $σ^{*}$ models whether a Gaussian should contribute to a particular object mask. This dual-opacity formulation decouples visual existence from instance occupancy: mislabeled Gaussians can remain available for image rendering while becoming transparent in the object-mask branch. To learn this representation, we introduce a random object loss that optimizes the 1D instance occupancy field using the standard transmittance-based visibility of 3DGS. Semantic descriptors are then attached at the object level through multi-view aggregation, eliminating per-Gaussian feature storage. Compared with feature-training approaches, OP2GS achieves competitive open-vocabulary performance while significantly reducing computational overhead. Compared with training-free pipelines, it leverages physically consistent occupancy learning to resolve visibility ambiguities.
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）
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）
Opacity-Gradient Driven Density Control for Compact and Efficient Few-Shot 3D Gaussian Splatting
Department of Computer Science and Physics, Wilfrid Laurier University（计算机科学与物理系，威尔弗里德·劳里尔大学）；Department of Computer Science（计算机科学系）；Physics, Wilfrid Laurier University（物理系，威尔弗里德·劳里尔大学）
TOGS: Gaussian Splatting with Temporal Opacity Offset for Real-Time 4D DSA Rendering
School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；Union Hospital, Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院附属协和医院）
本文提出 TOGS，通过随时间变化的 Gaussian 不透明度偏移表、Smooth loss 和随机剪枝，实现稀疏视角下高质量、低存储的实时 4D DSA 渲染。
PLEDGE: Progressive Lossy Error-Bounded Compression of 3D Gaussian Splatting
Oregon State University（俄勒冈州立大学）；The Ohio State University（俄亥俄州立大学）；Argonne National Laboratory（阿贡国家实验室）；The University of Texas Rio Grande Valley（德克萨斯大学里奥格兰德河谷分校）
Hunan University（湖南大学）；Nanyang Technological University（南洋理工大学）；Lushan Innovation Lab（庐山创新实验室）；Key Laboratory of Digital Culture Smart Design Technology, Minstry of Culture and Tourism（文化与旅游部数字文化智能设计技术重点实验室）
On the Geometric Accuracy of Implicit and Primitive-based Representations Derived from View Rendering Constraints
KU Leuven, Dept. Electrical Engineering, Research unit Processing Speech and Images（KU莱顿，电气工程系，语音与图像研究单位）；UCLouvain, ICTEAM, Dept. Electrical Engineering（UCLouvain，ICTEAM，电气工程系）；KU Leuven, Dept. Mechanical Engineering, Research unit Robotics, Automation and Mechatronics（KU莱顿，机械工程系，机器人、自动化与机电研究单位）
