Rendering 3D surfaces has been revolutionized within the modeling of radiance fields through either 3DGS or NeRF. Although 3DGS has shown advantages over NeRF in terms of rendering quality or speed, there is still room for improvement in recovering high fidelity surfaces through 3DGS. To resolve this issue, we propose a self-constrained prior to constrain the learning of 3D Gaussians, aiming for more accurate depth rendering. Our self-constrained prior is derived from a TSDF grid that is obtained by fusing the depth maps rendered with current 3D Gaussians. The prior measures a distance field around the estimated surface, offering a band centered at the surface for imposing more specific constraints on 3D Gaussians, such as removing Gaussians outside the band, moving Gaussians closer to the surface, and encouraging larger or smaller opacity in a geometry-aware manner. More importantly, our prior can be regularly updated by the most recent depth images which are usually more accurate and complete. In addition, the prior can also progressively narrow the band to tighten the imposed constraints. We justify our idea and report our superiority over the state-of-the-art methods in evaluations on widely used benchmarks.
GBR: Generative Bundle Refinement for High-fidelity Gaussian Splatting with Enhanced Mesh Reconstruction
College of future information technology, Fudan University（未来信息科技学院，复旦大学）；School of Biomedical Engineering, Tsinghua University（生物医学工程学院，清华大学）；Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University（电磁波信息科学重点实验室（MoE），复旦大学）；Department of Automation, Tsinghua University（自动化系，清华大学）；MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University（人工智能重点实验室，人工智能研究院，上海交通大学）
GARO: Geometry-Aware Redundancy Optimization for Real-Time and High-Fidelity Dynamic Gaussian Splatting
Northwestern Polytechnical University（西北工业大学）；Université Sorbonne Paris Nord（巴黎第十三大学）；EmboMind Research（EmboMind研究院）；Suzhou University of Science and Technology（苏州科技大学）
SecureGS: Boosting the Security and Fidelity of 3D Gaussian Splatting Steganography
School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology, Shenzhen Graduate School, Peking University（北京大学深圳研究生院广东省超高清沉浸式媒体技术重点实验室）
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）
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）
3D Gaussian Splatting against Moving Objects for High-Fidelity Street Scene Reconstruction
Ontario Knowledge Intelligence Centre, Canada（加拿大安大略知识智能中心）；Canadian Institute of Interscience, Canada（加拿大跨学科科学研究所）；DeepCox Intelligence, Canada（加拿大DeepCox智能科技公司）
Predictive Photometric Uncertainty in Gaussian Splatting for Novel View Synthesis
QUT Centre for Robotics, Australia（昆士兰大学机器人中心，澳大利亚）；ARIAM Hub, Australia（ARIAM中心，澳大利亚）；University of Wuppertal, Germany（乌尔姆大学，德国）；University of Osnabrück, Germany（奥斯纳布吕克大学，德国）
PrismGS: Physically-Grounded Anti-Aliasing for High-Fidelity Large-Scale 3D Gaussian Splatting
School of Information Science and Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China（上海交通大学信息科学与电子工程学院）；Cooperative Mediant Innovation Center, Shanghai Jiao Tong University, Shanghai, China（上海交通大学协同创新中心）；College of Information Science and Technology, Eastern Institute of Technology, Ningbo, China（宁波工程技术学院信息科学与技术学院）
PrismGS提出物理基础正则化框架，通过金字塔多尺度监督和尺寸下界约束，解决大规模3DGS在高分辨率下的锯齿与闪烁问题，实现即插即用，在多个城市数据集上较CityGaussian提升约1.5 dB PSNR。
