With the rapid advancement of 3D visualization, 3D Gaussian Splatting (3DGS) has emerged as a leading technique for real-time, high-fidelity rendering. While prior research has emphasized algorithmic performance and visual fidelity, the perceptual quality of 3DGS-rendered content, especially under varying reconstruction conditions, remains largely underexplored. In practice, factors such as viewpoint sparsity, limited training iterations, point downsampling, noise, and color distortions can significantly degrade visual quality, yet their perceptual impact has not been systematically studied. To bridge this gap, we present 3DGS-QA, the first subjective quality assessment dataset for 3DGS. It comprises 225 degraded reconstructions across 15 object types, enabling a controlled investigation of common distortion factors. Based on this dataset, we introduce a no-reference quality prediction model that directly operates on native 3D Gaussian primitives, without requiring rendered images or ground-truth references. Our model extracts spatial and photometric cues from the Gaussian representation to estimate perceived quality in a structure-aware manner. We further benchmark existing quality assessment methods, spanning both traditional and learning-based approaches. Experimental results show that our method consistently achieves superior performance, highlighting its robustness and effectiveness for 3DGS content evaluation. The dataset and code are made publicly available at https://github.com/diaoyn/3DGSQA to facilitate future research in 3DGS quality assessment.
GFix: Perceptually Enhanced Gaussian Splatting Video Compression
Visual Information Laboratory, University of Bristol（布里斯托大学视觉信息实验室）；University of Bristol（布里斯托大学）；School of Informatics, University of Edinburgh（爱丁堡大学信息学院）；Visionular Inc.（Visionular公司）
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
Evaluating Human Perception of Novel View Synthesis: Subjective Quality Assessment of Gaussian Splatting and NeRF in Dynamic Scenes
College of Electronics and Information Engineering, Shenzhen University（深圳大学电子与信息工程学院）；School of Electronics and Communication Engineering, Guangzhou University（广州大学电子与通信工程学院）；Univ Rennes（雷恩大学）；INSA Rennes（雷恩国立应用科学学院）；CNRS（法国国家科学研究中心）；IRT b < > absent <> < > com；IETR - UMR 6164
RePaintGS: Reference-Guided Gaussian Splatting for Realistic and View-Consistent 3D Scene Inpainting
Intelligence and Interaction Research Center, Korea Institute of Science and Technology（智能交互研究所，韩国科学技术院）；Department of Computer Science and Engineering, Korea University（韩国大学计算机科学与工程系）；AI-Robotics, KIST School, Korea National University of Science and Technology（人工智能机器人，KIST学院，韩国科学技术大学）
A Benchmark for Gaussian Splatting Compression and Quality Assessment Study
School of Science and Engineering, University of Missouri - Kansas City（密苏里大学堪萨斯城分校科学与工程学院）；Cooperative Medianet Innovation Center, Shanghai Jiaotong University（上海交通大学合作 medianet 创新中心）
GaussVid: Sparse-View Gaussian Splatting with 3D-Aware Video Diffusion Priors
The University of Hong Kong（香港大学）；School of Computing and Data Science, The University of Hong Kong（香港大学计算与数据科学学院）；Futurewei Technologies Inc（华为主流技术公司）
Triangle Splatting for Real-Time Radiance Field Rendering
University of Liège（列日大学）；KAUST（阿卜杜拉国王科技大学）；University of Oxford（牛津大学）；Simon Fraser University（西蒙弗雷泽大学）；University of Toronto（多伦多大学）；Google DeepMind（谷歌DeepMind）
FlexSplat: Flexible Feed-Forward 3D Gaussian Splatting without Point Cloud Correspondence
Tri-Institutional Georgia Institute of Technology/Georgia State University/Emory University Center for Translational Research in Data Science and Neuroimaging (TReNDS)（三机构佐治亚理工学院/佐治亚州立大学/埃默里大学转化数据科学与神经影像中心（TReNDS））；Georgia State University（佐治亚州立大学）；Duke University（杜克大学）；University of Tennessee, Knoxville（田纳西大学诺克斯维尔分校）
Université de Tours（图尔大学）；CNRS（法国国家科学研究中心）；IDP；Institut Universitaire de France (IUF)（法国大学研究院）；Instituto de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de la República（乌拉圭共和国大学工程学院电气工程研究所）；City University of Hong Kong（香港城市大学）
ClearGS: Reliability-Aware Gaussian Splatting from Handheld Videos
Shenzhen University of Advanced Technology（深圳理工大学）；HONOR（荣耀）；University of New South Wales（新南威尔士大学）；Southern University of Science and Technology（南方科技大学）
Point clouds are essential for storage and transmission of 3D content. As they can entail significant volumes of data, point cloud compression is crucial for practical usage. Recently, point cloud geometry compression approaches based on deep neural networks have been explored. In this paper, we evaluate the ability to predict perceptual quality of typical voxel-based loss functions employed to train these networks. We find that the commonly used focal loss and weighted binary cross entropy are poorly correlated with human perception. We thus propose a perceptual loss function for 3D point clo…
The field of automatic image inpainting has progressed rapidly in recent years, but no one has yet proposed a standard method of evaluating algorithms. This absence is due to the problem's challenging nature: image-inpainting algorithms strive for realism in the resulting images, but realism is a subjective concept intrinsic to human perception. Existing objective image-quality metrics provide a poor approximation of what humans consider more or less realistic. To improve the situation and to better organize both prior and future research in this field, we conducted a subjective comparison of …
