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
Yuhang Zhang, Joshua Maraval, Zhengyu Zhang, Nicolas Ramin, Shishun Tian, Lu Zhang
Gaussian Splatting (GS) and Neural Radiance Fields (NeRF) are two groundbreaking technologies that have revolutionized the field of Novel View Synthesis (NVS), enabling immersive photorealistic rendering and user experiences by synthesizing multiple viewpoints from a set of images of sparse views. The potential applications of NVS, such as high-quality virtual and augmented reality, detailed 3D modeling, and realistic medical organ imaging, underscore the importance of quality assessment of NVS methods from the perspective of human perception. Although some previous studies have explored subjective quality assessments for NVS technology, they still face several challenges, especially in NVS methods selection, scenario coverage, and evaluation methodology. To address these challenges, we conducted two subjective experiments for the quality assessment of NVS technologies containing both GS-based and NeRF-based methods, focusing on dynamic and real-world scenes. This study covers 360°, front-facing, and single-viewpoint videos while providing a richer and greater number of real scenes. Meanwhile, it's the first time to explore the impact of NVS methods in dynamic scenes with moving objects. The two types of subjective experiments help to fully comprehend the influences of different viewing paths from a human perception perspective and pave the way for future development of full-reference and no-reference quality metrics. In addition, we established a comprehensive benchmark of various state-of-the-art objective metrics on the proposed database, highlighting that existing methods still struggle to accurately capture subjective quality. The results give us some insights into the limitations of existing NVS methods and may promote the development of new NVS methods.
NG-GS: NeRF-Guided 3D Gaussian Splatting Segmentation
Key Laboratory of Big Data and Artificial Intelligence in Transportation, Ministry of Education（交通大数据与人工智能联合实验室，教育部）；School of Computer and Information Technology, Beijing Jiaotong University（北京交通大学计算机与信息学院）；Department of Artificial Intelligence, Westlake University（西湖大学人工智能学院）
Explicit-NeRF-QA: A Quality Assessment Database for Explicit NeRF Model Compression
Cooperative MediaNet Innovation Center, Shanghai Jiao Tong University（上海交通大学合作媒体网络创新中心）；Department of Computer Science and Electrical Engineering, University of Missouri-Kansas City（密苏里大学堪萨斯城分校计算机科学与电气工程系）
A Survey on 3D Reconstruction Techniques in Plant Phenotyping: From Classical Methods to Neural Radiance Fields (NeRF), 3D Gaussian Splatting (3DGS), and Beyond
Department of Electrical and Computer Engineering, Michigan State University（电气与计算机工程系，密歇根州立大学）；Department of Systems and Information Engineering & Link Lab, University of Virginia（系统与信息工程系及Link实验室，弗吉尼亚大学）；Department of Statistics and Probability, Michigan State University（统计与概率系，密歇根州立大学）；School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University（化学、化工与生物技术学院，南洋理工大学）；Department of Agricultural and Biological Engineering, Mississippi State University（农业与生物工程系，密西西比州立大学）；Department of Mechanical Engineering, Michigan State University（机械工程系，密歇根州立大学）
Institute for Intelligent Systems, Esslingen University of Applied Sciences（埃斯林根应用科学大学智能系统研究所）；Department of Computer Science, University of Freiburg（弗莱堡大学计算机科学系）
Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague（捷克布拉格技术大学捷克信息学、机器人学与控制论研究所）；ETH Zurich（苏黎世联邦理工学院）；Google DeepMind（谷歌DeepMind）；Faculty of Electrical Engineering, Czech Technical University in Prague（捷克布拉格技术大学电气工程学院）
DenseSplat: Densifying Gaussian Splatting SLAM with Neural Radiance Prior
Department of Computer Science, Dalian University of Technology（大连理工大学计算机科学系）；Department of Mechano-informatics, Information Science and Technology, The University of Tokyo（东京大学机械信息学系、信息科学与技术系）；Institute of Medical Robotics and Department of Automation, Shanghai Jiao Tong University（上海交通大学医学机器人研究所、自动化系）；Key Laboratory of System Control and Information Processing, Ministry of Education（教育部系统控制与信息处理重点实验室）
MVPGS: Excavating Multi-view Priors for Gaussian Splatting from Sparse Input Views
School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；School of Computer Science, University of Birmingham（伯明翰大学计算机学院）；Peng Cheng Laboratory（鹏城实验室）
EOGS++: Earth Observation Gaussian Splatting with Internal Camera Refinement and Direct Panchromatic Rendering
Universite Paris-Saclay, CNRS, ENS Paris-Saclay, Centre Borelli（巴黎萨克雷大学，法国国家科学研究中心，巴黎萨克雷高等师范学校，博雷利中心）；Politecnico di Torino（都灵理工大学）；AMIAD, Pôle Recherche（AMIAD，研究部）；Institut Universitaire de France（法兰西大学研究院）
PLGS: Robust Panoptic Lifting with 3D Gaussian Splatting
School of Automation Science and Electrical Engineering, Beihang University（北京航空航天大学自动化科学与电气工程学院）；Institute of Software, Chinese Academy of Sciences & University of Chinese Academy of Sciences（中国科学院软件研究所）；Peking University（北京大学）
iGaussian: Real-Time Camera Pose Estimation via Feed-Forward 3D Gaussian Splatting Inversion
School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications（智能工程与自动化学院，北京邮电大学）；Department of Automation, Tsinghua University（自动化系，清华大学）
