3D Gaussian Splatting has recently been embraced as a versatile and effective method for scene reconstruction and novel view synthesis, owing to its high-quality results and compatibility with hardware rasterization. Despite its advantages, Gaussian Splatting's reliance on high-quality point cloud initialization by Structure-from-Motion (SFM) algorithms is a significant limitation to be overcome. To this end, we investigate various initialization strategies for Gaussian Splatting and delve into how volumetric reconstructions from Neural Radiance Fields (NeRF) can be utilized to bypass the dependency on SFM data. Our findings demonstrate that random initialization can perform much better if carefully designed and that by employing a combination of improved initialization strategies and structure distillation from low-cost NeRF models, it is possible to achieve equivalent results, or at times even superior, to those obtained from SFM initialization. Source code is available at https://theialab.github.io/nerf-3dgs .
AquaGS: Fast Underwater Scene Reconstruction with SfM-Free Gaussian Splatting
School of Electronic Information and Electrical Engineering（电子信息与电气工程学院）；Shanghai Jiao Tong University（上海交通大学）；State Key Laboratory of Ocean Engineering（海洋工程国家重点实验室）
Enhancing Novel View Synthesis from extremely sparse views with SfM-free 3D Gaussian Splatting Framework
Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University（电子与电气工程系，香港理工大学）；School of Intelligent Systems Engineering, Sun Yat-sen University（智能系统工程学院，中山大学）
Segmentation-Driven Initialization for Sparse-view 3D Gaussian Splatting
Mid Sweden University（中瑞典大学）；Technical University of Berlin（柏林技术大学）；Hochschule für Technik und Wirtschaft Berlin（柏林技术与经济高等学院）；Department of Computer and Electrical Engineering, Mid Sweden University（中瑞典大学计算机与电气工程系）；Department of Telecommunication Systems, Technical University of Berlin（柏林技术大学电信系统系）
The Role of Initialization in 3D Gaussian Splatting
Czech Technical University in Prague, Faculty of Electrical Engineering（布拉格捷克技术大学电子工程系）；Czech Technical University in Prague, Czech Institute of Informatics, Robotics and Cybernetics（布拉格捷克技术大学信息技术、机器人学与自动化研究所）
SalientGS: Unified SfM-to-3DGS with Importance-Guided MCMC Gaussian Allocation
School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；CEMSE, King Abdullah University of Science and Technology（阿卜杜拉国王科技大学计算、电子与数学科学和工程学院）
GaussianPro: 3D Gaussian Splatting with Progressive Propagation
University of Science and Technology of China（中国科学技术大学）；The University of Hong Kong（香港大学）；Nanjing University（南京大学）；The University of Adelaide（阿德莱德大学）；ShanghaiTech University（上海科技大学）；Texas A&M University（德克萨斯农工大学）
3D Gaussian Splatting with Fisheye Images: Field of View Analysis and Depth-Based Initialization
Department of Computing Sciences, Tampere University（塔尔基大学计算机科学系）；Department of Computer Science, Aalto University（阿尔托大学计算机科学系）；Department of Computer Science and Engineering, University of Oulu（奥卢大学计算机科学与工程系）
InstantSplat: Sparse-view Gaussian Splatting in Seconds
University of Texas at Austin（德克萨斯大学）；Nvidia Research（英伟达研究）；Xiamen University（厦门大学）；Georgia Institute of Technology（佐治亚理工学院）；Stanford University（斯坦福大学）；University of Southern California（南加州大学）
GeoSplat: A Deep Dive into Geometry-Constrained Gaussian Splatting
Department of Applied Mathematics and Theoretical Physics, University of Cambridge（应用数学与理论物理系，剑桥大学）；Institut Camille Jordan, Université Claude Bernard Lyon 1（让·Camille Jordan研究所，克莱蒙费朗斯大学）
Pose-Free Omnidirectional Gaussian Splatting for 360-Degree Videos with Consistent Depth Priors
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Chongqing University（重庆大学）；Air Force Engineering University（空军工程大学）
MeshSplats: Mesh-Based Rendering with Gaussian Splatting Initialization
Faculty of Mathematics and Computer Science, Jagiellonian University in Kraków（克拉科夫雅盖隆大学数学与计算机科学学院）；Department of Engineering, University of Cambridge（剑桥大学工程系）；IDEAS Research Institute（IDEAS研究所）
University of British Columbia（不列颠哥伦比亚大学）；Google Research（谷歌研究院）；Google DeepMind（谷歌DeepMind）；Simon Fraser University（西蒙菲莎大学）；University of Toronto（多伦多大学）
