3D Gaussian Splatting (3DGS) has shown remarkable performance in novel view synthesis. However, its rendering quality deteriorates with sparse inphut views, leading to distorted content and reduced details. This limitation hinders its practical application. To address this issue, we propose a sparse-view 3DGS method. Given the inherently ill-posed nature of sparse-view rendering, incorporating prior information is crucial. We propose a semantic regularization technique, using features extracted from the pretrained DINO-ViT model, to ensure multi-view semantic consistency. Additionally, we propose local depth regularization, which constrains depth values to improve generalization on unseen views. Our method outperforms state-of-the-art novel view synthesis approaches, achieving up to 0.4dB improvement in terms of PSNR on the LLFF dataset, with reduced distortion and enhanced visual quality.
NRGS: Neural Regularization for Robust 3D Semantic Gaussian Splatting
Beijing University of Posts and Telecommunications, China（北京邮电大学，中国）；Beijing Key Laboratory of Multimodal Data Intelligent Perception and Governance, China（北京多模态数据智能感知与治理重点实验室，中国）；The University of Osaka, Japan（大阪大学，日本）；Nankai University, China（南开大学，中国）
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）
FreGS: 3D Gaussian Splatting with Progressive Frequency Regularization
Nanyang Technological University（南洋理工大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Carnegie Mellon University（卡内基梅隆大学）；MBZUAI（穆罕默德·本·扎耶德人工智能大学）
DET-GS: Depth- and Edge-Aware Regularization for High-Fidelity 3D Gaussian Splatting
Perceptual Imaging Laboratory (PILab), School of Electrical and Data Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney（感知成像实验室（PILab）、电气与数据工程学院、工程与信息技术学院、新南威尔士大学）
SVR-GS: Spatially Variant Regularization for Probabilistic Masks in 3D Gaussian Splatting
Department of Computer Science and Software Engineering, The University of Western Australia（西澳大学计算机科学与软件工程系）；Dolby Laboratories（Dolby实验室）；Department of Electrical, Electronics and Computer Engineering, The University of Western Australia（西澳大学电气、电子与计算机工程系）；School of Information Technology, Murdoch University（穆尔德大学信息技术学院）
Topology-aware Human Avatars with Semantically-guided Gaussian Splatting
School of Computer Science, Wuhan University（武汉大学计算机学院）；School of Software Engineering, Xi’an Jiao Tong University（西安交通大学软件学院）；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）
Differentiable image sampling in the form of backward warping has seen broad adoption in tasks like depth estimation and optical flow prediction. In contrast, how to perform forward warping has seen less attention, partly due to additional challenges such as resolving the conflict of mapping multiple pixels to the same target location in a differentiable way. We propose softmax splatting to address this paradigm shift and show its effectiveness on the application of frame interpolation. Specifically, given two input frames, we forward-warp the frames and their feature pyramid representations b…
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）
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）
Rendering Anywhere You See: Renderability Field-guided Gaussian Splatting
Laboratory of Artificial Intelligence Robotics（人工智能机器人实验室）；Politecnico di Milano（米兰理工学院）；Ningbo Institute of Materials Technology and Engineering（宁波材料技术与工程研究所）
Seeing Through Reflections: Advancing 3D Scene Reconstruction in Mirror-Containing Environments with Gaussian Splatting
School of Automation and Intelligent Sening, Shanghai Jiao Tong University（自动化与智能传感学院，上海交通大学）；Ningbo Artificial Intelligence Institute, Shanghai Jiao Tong University（宁波人工智能研究所，上海交通大学）
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）
