The field of novel view synthesis from images has seen rapid advancements with the introduction of Neural Radiance Fields (NeRF) and more recently with 3D Gaussian Splatting. Gaussian Splatting became widely adopted due to its efficiency and ability to render novel views accurately. While Gaussian Splatting performs well when a sufficient amount of training images are available, its unstructured explicit representation tends to overfit in scenarios with sparse input images, resulting in poor rendering performance. To address this, we present a 3D Gaussian-based novel view synthesis method using sparse input images that can accurately render the scene from the viewpoints not covered by the training images. We propose a multi-stage training scheme with matching-based consistency constraints imposed on the novel views without relying on pre-trained depth estimation or diffusion models. This is achieved by using the matches of the available training images to supervise the generation of the novel views sampled between the training frames with color, geometry, and semantic losses. In addition, we introduce a locality preserving regularization for 3D Gaussians which removes rendering artifacts by preserving the local color structure of the scene. Evaluation on synthetic and real-world datasets demonstrates competitive or superior performance of our method in few-shot novel view synthesis compared to existing state-of-the-art methods.
M^3: Dense Matching Meets Multi-View Foundation Models for Monocular Gaussian Splatting SLAM
Shanghai Jiao Tong University（上海交通大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Fudan University（复旦大学）；Shanghai Innovation Institute（上海创新研究院）；Zhejiang University（浙江大学）；Beijing Institute of Technology（北京理工大学）；The Chinese University of Hong Kong（香港中文大学）；The University of Hong Kong（香港大学）
Capacity-Controlled Multi-View Stylization of 3D Gaussian Splatting
Guangdong Provincial Key Laboratory of Visual Media and Multidimensional Intelligence, CSSE, Shenzhen University（广东省可视媒体与多维智能重点实验室，计算机科学与软件工程学院，深圳大学）；Tencent Games（腾讯游戏）；The Hebrew University of Jerusalem（耶路撒冷希伯来大学）；Tel Aviv University（特拉维夫大学）
Aerial-Ground Image Feature Matching via 3D Gaussian Splatting-based Intermediate View Rendering
organization= School of Computer Science , addressline= China University of Geosciences , city= Wuhan , postcode= 430074 , country= China；organization= Guangdong Key Laboratory of Urban Informatics , addressline= Shenzhen University , city= Guangdong Shenzhen , postcode= 518060 , country= China；organization= MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area , addressline= Shenzhen University , city= Guangdong Shenzhen , postcode= 518060 , country= China；organization= Engineering Research Center of Natural Resource Information Management
School of Computer Science and Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）；School of Software Technology, Zhejiang University（浙江大学软件学院）
Local-GS: Accelerating 3D Gaussian Splatting via Tile-Local Warp Coherence
State Key Laboratory of Robotics and Systems, Harbin Institute of Technology（哈尔滨工业大学机器人技术与系统国家重点实验室）；State Key Laboratory of Intelligent Green Vehicle and Mobility, School of Vehicle and Mobility, Tsinghua University（清华大学车辆与运载学院智能绿色车辆与交通国家重点实验室）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
GS-Octree: Octree-based 3D Gaussian Splatting for Robust Object-level 3D Reconstruction Under Strong Lighting
College of Computing and Data Science（计算与数据科学学院）；Nanyang Technological University（南洋理工大学）；International School of Information Science & Engineering（信息科学与工程国际学院）；Dalian University of Technology（大连理工大学）；Institute of Software（软件研究所）；Chinese Academy of Sciences（中国科学院）；InnoPeak Technology, Inc.（InnoPeak科技公司）
ProSplat: Improved Feed-Forward 3D Gaussian Splatting for Wide-Baseline Sparse Views
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University（多媒体信息处理国家重点实验室，计算机科学学院，北京大学）；State Key Laboratory of Multimedia Information Processing, School of Electronic and Computer Engineering, Peking University（多媒体信息处理国家重点实验室，电子与计算机工程学院，北京大学）；Wangxuan Institute of Computer Technology, State Key Laboratory of Multimedia Information Processing, Peking University（王轩计算机技术研究所，多媒体信息处理国家重点实验室，北京大学）
SuperGS: Consistent and Detailed 3D Super-Resolution Scene Reconstruction via Gaussian Splatting
School of Artificial Intelligence, Beihang University（人工智能学院，北京航空航天大学）；Sino-French Engineer School, Beihang University（中法工程师学院，北京航空航天大学）；Institute of Unmanned System, Beihang University（无人系统研究院，北京航空航天大学）
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）
