S2Gaussian: Sparse-View Super-Resolution 3D Gaussian Splatting
China University of Petroleum (East China)（中国石油大学（华东））；College of Computer Science and Technology（计算机科学与技术学院）；Qingdao Institute of Software（青岛软件学院）；Harbin Institute of Technology（哈尔滨工业大学）；Faculty of Computing（计算机学院）
In this paper, we aim ambitiously for a realistic yet challenging problem, namely, how to reconstruct high-quality 3D scenes from sparse low-resolution views that simultaneously suffer from deficient perspectives and clarity. Whereas existing methods only deal with either sparse views or low-resolution observations, they fail to handle such hybrid and complicated scenarios. To this end, we propose a novel Sparse-view Super-resolution 3D Gaussian Splatting framework, dubbed S2Gaussian, that can reconstruct structure-accurate and detail-faithful 3D scenes with only sparse and low-resolution views. The S2Gaussian operates in a two-stage fashion. In the first stage, we initially optimize a low-resolution Gaussian representation with depth regularization and densify it to initialize the high-resolution Gaussians through a tailored Gaussian Shuffle Split operation. In the second stage, we refine the high-resolution Gaussians with the super-resolved images generated from both original sparse views and pseudo-views rendered by the low-resolution Gaussians. In which a customized blur-free inconsistency modeling scheme and a 3D robust optimization strategy are elaborately designed to mitigate multi-view inconsistency and eliminate erroneous updates caused by imperfect supervision. Extensive experiments demonstrate superior results and in particular establishing new state-of-the-art performances with more consistent geometry and finer details.
SolidGS: Consolidating Gaussian Surfel Splatting for Sparse-View Surface Reconstruction
Texas A&M University（德克萨斯农工大学）；OPPO US Research Center（OPPO美国研究中心）；Nanyang Technological University（南洋理工大学）；Hong Kong University of Science and Technology（香港科技大学）；The University of Hong Kong（香港大学）
SparseStreet: Sparse Gaussian Splatting for Real-Time Street Scene Simulation
Peking University（北京大学）；Chinese Academy of Sciences（中国科学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Autonomous Driving Development, NIO（蔚来自动驾驶开发）
AD-GS: Anatomical Density-guided 3D Gaussian Splatting for Sparse-View Dental CBCT Reconstruction
Beijing University of Posts and Telecommunications（北京邮电大学）；Peking University Third Hospital（北京大学第三医院）；National Institute of Informatics（信息学研究所）；Peking University（北京大学）
Learning Fine-Grained Geometry for Sparse-View Splatting via Cascade Depth Loss
School of Computer Science The University of Sydney Sydney, Australia；School of Computer Science University of Bristol Bristol, United Kingdom；School of Science Edith Cowan University Perth, Australia
LGDWT-GS: Local and Global Discrete Wavelet-Regularized 3D Gaussian Splatting for Sparse-View Scene Reconstruction
National Institute of Standards and Technology（国家标准与技术研究院）；Department of Physics, Colorado State University（科罗拉多州立大学物理系）；Electrical Engineering Department, University of Colorado（科罗拉多大学电气工程系）
SparseLGS: Sparse View Language Embedded Gaussian Splatting
School of Mathematical Science, University of Science and Technology of China（科学技术大学数学科学学院）；Meta；Apple Inc.（苹果公司）；Alpha Labs at Goertek（Goertek的Alpha实验室）
3D-Consistent Human Avatars with Sparse Inputs via Gaussian Splatting and Contrastive Learning
School of Computer Science, Wuhan University（武汉大学计算机学院）；AI Institute, Shanghai Jiao Tong University（上海交通大学人工智能研究院）；Huazhong University of Science and Technology（华中科技大学）；Wuhan National Laboratory for Optoelectronics（武汉光电国家实验室）
Geometry-Consistent 4D Gaussian Splatting for Sparse-Input Dynamic View Synthesis
The Hong Kong Polytechnic University, Hong Kong, China（香港理工大学）；Southern University of Science and Technology, Shenzhen, China（南方科技大学）；Indian Institute of Technology, Jodhpur, India（印度理工学院，朱道尔）；Institute of Cyberspace Technology, HKCT Institute of Higher Education, Hong Kong, China（网络空间技术研究所，香港高等教育学院）
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）
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）
D$^2$GS: Depth-and-Density Guided Gaussian Splatting for Stable and Accurate Sparse-View Reconstruction
Insta360 Research（Insta360研究机构）；Tsinghua University（清华大学）；University of California, San Diego（加州大学圣地亚哥分校）；Nanyang Technological University（南洋理工大学）；Wuhan University（武汉大学）
OracleGS: Grounding Generative Priors for Sparse-View Gaussian Splatting
ETH Zürich（苏黎世联邦理工学院）；Koç University（科卡大学）；KUIS AI Center（KUIS人工智能中心）；Technical University of Munich（慕尼黑技术大学）；Google（谷歌）；Munich Center for Machine Learning（慕尼黑机器学习中心）
