Gaussian Splatting (GS) has recently emerged as a promising technique for 3D object reconstruction, delivering high-quality rendering results with significantly improved reconstruction speed. As variants continue to appear, assessing the perceptual quality of 3D objects reconstructed with different GS-based methods remains an open challenge. To address this issue, we first propose a unified multi-distance subjective quality assessment method that closely mimics human viewing behavior for objects reconstructed with GS-based methods in actual applications, thereby better collecting perceptual experiences. Based on it, we also construct a novel GS quality assessment dataset named MUGSQA, which is constructed considering multiple uncertainties of the input data. These uncertainties include the quantity and resolution of input views, the view distance, and the accuracy of the initial point cloud. Moreover, we construct two benchmarks: one to evaluate the robustness of various GS-based reconstruction methods under multiple uncertainties, and the other to evaluate the performance of existing quality assessment metrics. Our dataset and code are available at https://github.com/Solivition/MUGSQA.
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）
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
Comparative evaluation of photogrammetric reconstruction methods and 3D Gaussian Splatting for road surface roughness analysis
School of International Education, Hubei University of Technology（湖北工业大学国际教育学院）；School of Computer Science, Hubei University of Technology（湖北工业大学计算机科学学院）
GARO: Geometry-Aware Redundancy Optimization for Real-Time and High-Fidelity Dynamic Gaussian Splatting
Northwestern Polytechnical University（西北工业大学）；Université Sorbonne Paris Nord（巴黎第十三大学）；EmboMind Research（EmboMind研究院）；Suzhou University of Science and Technology（苏州科技大学）
Adaptive 3D Gaussian Splatting Video Streaming: Visual Saliency-Aware Tiling and Meta-Learning-Based Bitrate Adaptation
School of Electrical Engineering and Automation, Hefei University of Technology（电子工程与自动化学院，合肥工业大学）；Engineering Technology Research Center of Industrial Automation of Anhui Province（安徽省工业自动化工程技术研究中心）；School of Computer Science and Information Engineering, Hefei University of Technology（计算机科学与信息工程学院，合肥工业大学）；The University of Electro-Communications（电子通信大学）
4DRGS: 4D Radiative Gaussian Splatting for Efficient 3D Vessel Reconstruction from Sparse-View Dynamic DSA Images
State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University（上海科技大学先进医用材料与器件国家重点实验室）；School of Computing, Australian National University（澳大利亚国立大学计算学院）；School of Computer Science, Wuhan University（武汉大学计算机学院）
GauSS-MI: Gaussian Splatting Shannon Mutual Information for Active 3D Reconstruction
School of Computing and Data Science, The University of Hong Kong（计算与数据科学学院，香港大学）；Division of Robotics, Perception, and Learning, KTH Royal Institute of Technology（机器人、感知与学习 division，皇家理工学院）；Faculty of Engineering, The University of Hong Kong（工程学院，香港大学）；Centre for Transformative Garment Production, Hong Kong SAR, China（变革性服装生产中心，香港特别行政区，中国）
GVGEN: Text-to-3D Generation with Volumetric Representation
Zhejiang University（浙江大学）；VAST；The Chinese University of Hong Kong（香港中文大学）；Shanghai AI Laboratory（上海人工智能实验室）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Shanghai Jiao Tong University（上海交通大学）
NVS-SQA: Exploring Self-Supervised Quality Representation Learning for Neurally Synthesized Scenes without References
School of Computer Science, the University of Sydney（悉尼大学计算机科学学院）；School of Software, Shandong University（山东大学软件学院）；School of Computer and Artificial Intelligence, Beijing Technology and Business University（北京科技与商业大学计算机与人工智能学院）
Comparative Analysis of Novel View Synthesis and Photogrammetry for 3D Forest Stand Reconstruction and extraction of individual tree parameters
Fuzhou University（福州大学）；National Engineering Research Center of Geospatial Information Technology（国家地理空间信息技术工程研究中心）；Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education（教育部空间数据挖掘与信息共享重点实验室）；The Academy of Digital China (Fujian)（数字中国研究院（福建））
