3D Gaussian Splatting (3DGS) has revolutionized novel-view synthesis with its fast and high-fidelity rendering. However, rendering at high FPS and low latency across various scenes remains a challenge, especially when large amounts of 3D Gaussian ellipsoids appear in the scene. To address this issue, we introduce TemporalGS, to the best of our knowledge, the first training-free plug-and-play algorithmic approach to accelerate 3DGS rendering without any post-training or post-processing, implemented on top of tile-based software rasterization. The key idea is that, instead of rendering frames independently as 3DGS, we leverage the temporal priors, represented by novel geometry and appearance buffers, etc., to reduce redundancy of Gaussian preprocessing, sorting, and rasterization operations of consecutive frames. Specifically, we propose two acceleration strategies: (1) temporal dynamic culling, which filters out Gaussians that contribute less to current frame rendering; (2) selective rendering, which renders only a small portion of tiles that cannot be approximated by the temporal priors. By adapting and interleaving these two strategies, TemporalGS yields a simple but effective plug-and-play solution for 3DGS rendering speed-up without any training. Extensive experiments show that TemporalGS achieves comparable or even better performance compared to existing state-of-the-art post-training or post-processing-based 3DGS rendering acceleration approaches. TemporalGS can significantly enhance the rendering speed of various 3DGS methods, achieving up to $1.48\times$ acceleration, while maintaining competitive rendering quality. We further extend our TemporalGS to hardware rasterization-based 3DGS to show the portability of our algorithm.
GSCompleter: A Distillation-Free Plugin for Metric-Aware 3D Gaussian Splatting Completion in Seconds
School of Computer Science and Technology, East China Normal University（东华大学计算机科学与技术学院）；Shanghai Innovation Institute（上海创新研究院）；Chongqing Key Laboratory of Precision Optics, Chongqing Institute of East China Normal University（重庆精密光学重点实验室，东华大学重庆研究院）；Shanghai Key Laboratory of Computer Software Evaluating and Testing（上海计算机软件评测测试重点实验室）；Department of Computer Science and Engineering, Shanghai Jiao Tong University（上海交通大学计算机科学与工程学院）
Predictive Photometric Uncertainty in Gaussian Splatting for Novel View Synthesis
QUT Centre for Robotics, Australia（昆士兰大学机器人中心，澳大利亚）；ARIAM Hub, Australia（ARIAM中心，澳大利亚）；University of Wuppertal, Germany（乌尔姆大学，德国）；University of Osnabrück, Germany（奥斯纳布吕克大学，德国）
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）
Local-GS: Accelerating 3D Gaussian Splatting via Tile-Local Warp Coherence
State Key Laboratory of Robotics and Systems, Harbin Institute of Technology（哈尔滨工业大学机器人技术与系统国家重点实验室）；State Key Laboratory of Intelligent Green Vehicle and Mobility, School of Vehicle and Mobility, Tsinghua University（清华大学车辆与运载学院智能绿色车辆与交通国家重点实验室）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
PairDropGS: Paired Dropout-Induced Consistency Regularization for Sparse-View Gaussian Splatting
School of Computer Science, Harbin Institute of Technology, Shenzhen, China（哈尔滨工业大学深圳校区计算机科学学院，中国）；Pengcheng Laboratory, Shenzhen, China（鹏城实验室，中国）；Smart Coding Institute, Pengcheng Laboratory, Shenzhen, China（鹏城实验室智能编码研究所，中国）；School of Computer Science, Harbin Institute of Technology, Harbin, China（哈尔滨工业大学计算机科学学院，中国）
SPARE-GS: Structural Parsimony and Resource Efficiency for 3D Gaussian Splatting
School of Electronics and Information, Northwestern Polytechnical University（西北工业大学电子信息学院）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；School of Telecommunication Engineering, Xidian University（西安电子科技大学通信工程学院）；School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；Department of Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电气工程系）
EMD: Explicit Motion Modeling for High-Quality Street Gaussian Splatting
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University（信息处理国家重点实验室，计算机学院，北京大学）；Autonomous Driving Development, NIO（自动驾驶研发，蔚来）；Institute of Software, Chinese Academy of Sciences（软件研究所，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Intel Labs China（英特尔中国实验室）
PrismGS: Physically-Grounded Anti-Aliasing for High-Fidelity Large-Scale 3D Gaussian Splatting
School of Information Science and Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China（上海交通大学信息科学与电子工程学院）；Cooperative Mediant Innovation Center, Shanghai Jiao Tong University, Shanghai, China（上海交通大学协同创新中心）；College of Information Science and Technology, Eastern Institute of Technology, Ningbo, China（宁波工程技术学院信息科学与技术学院）
PrismGS提出物理基础正则化框架，通过金字塔多尺度监督和尺寸下界约束，解决大规模3DGS在高分辨率下的锯齿与闪烁问题，实现即插即用，在多个城市数据集上较CityGaussian提升约1.5 dB PSNR。
STDR: Spatio-Temporal Decoupling for Real-Time Dynamic Scene Rendering
Institute of Computing Technology, Chinese Academy of Sciences, ICT（中国科学院计算技术研究所）；University of Chinese Academy of Sciences, UCAS（中国科学院大学）；The University of Queensland（昆士兰大学）；University of California, Merced（加州大学梅尔塞德斯分校）
Visionary: The World Model Carrier Built on WebGPU-Powered Gaussian Splatting Platform
Shanghai AI Laboratory（上海人工智能实验室）；Sichuan University（四川大学）；The University of Tokyo（东京大学）；Shanghai Jiao Tong University（上海交通大学）；Northwestern Polytechnical University（西北工业大学）
AvatarBack: Back-Head Generation for Complete 3D Avatars from Front-View Images
College of Electrical Engineering and Automation, Shandong University of Science and Technology（山东科技大学电气工程与自动化学院）；Department of Data Science and Artificial Intelligence, Auckland University of Technology（奥克兰大学数据科学与人工智能系）；College of Computer Science and Engineering, Shandong University of Science and Technology（山东科技大学计算机科学与工程学院）
