3D Gaussian Splatting

Also known as 3DGS

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Latest papers 729

130 papers in the last four weeks, up 40% on the four weeks before. 0.9% of all new papers.

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From Uncertainty to Stability and Fidelity: Guiding Sparse-View 3D Gaussian Splatting with Fisher Information
Jun 18, 2026Junbao Zhou, Qingshan Xu, Yuan Zhou +73D Gaussian Splatting Novel View Synthesis
Nanyang Technological University, Singapore · University of Science and Technology of China, China · Zhejiang University, China +1
LIT-GS: LiDAR-Inertial-Thermal Gaussian Splatting for Illumination-Robust Mapping
Jun 18, 2026Shikuan Shi, Chunran Zheng, Jiaming Xu +3Simultaneous Localization and Mapping 3D Gaussian Splatting
College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China · Department of Mechanical Engineering, The University of Hong Kong, Hong Kong SAR, China
Geometry-Preserving in 3D Gaussian Splatting for LiDAR-Camera Extrinsic Calibration
Jun 18, 2026Kyoleen Kwak, Daeho Kim, Jeong Woon Lee +13D Gaussian Splatting Camera-LiDAR Fusion
Kyung Hee University
MMD-SLAM: Structure-Enhanced Multi-Meta Gaussian Distribution-Guided Visual SLAM
Jun 18, 2026Fan Zhu, Ziyu Chen, Peichen Liu +6Simultaneous Localization and Mapping 3D Gaussian Splatting
HFIPS, Chinese Academy of Sciences, Hefei, China. · University of Science and Technology of China, Hefei, China. · Aarhus University, Aarhus, Denmark. +3
Hand-4DGS: Feed-Forward 3D Gaussian Splatting for 4D Hand Reconstruction from Egocentric Videos
Jun 17, 2026Jeongmin Bae, Seoha Kim, Marc Pollefeys +33D Gaussian Splatting 4D Reconstruction
Yonsei University · Electronics and Telecommunications Research Institute · ETH Zurich +2
Do Gaussian Scenes Contain Enough Structure for Intrinsic Segmentation?
Jun 17, 2026Mohamed Rayan Barhdadi, Hasan Yazar, Erchin Serpedin +23D Gaussian Splatting Image Segmentation
Texas A&M University · Istanbul Technical University · Hamad Bin Khalifa University
Splaxel: Efficient Distributed Training of 3D Gaussian Splatting for Large-scale Scene Reconstruction via Pixel-level Communication
Jun 17, 2026Wenqi Jia, Zhewen Hu, Ying Huang +103D Gaussian Splatting 3DGS Acceleration
UT Arlington · Texas A&M University · Independent Researcher +5
MoonSplat: Monocular Online Gaussian Splatting with Sim(3) Global Optimization
Jun 16, 2026Guo Pu, Yixuan Han, Haofeng Li +33D Gaussian Splatting Language-Embedded 3DGS
Wangxuan Institute of Computer Technology, Peking University, China · Beijing Hydrogen Intelligent Tech. Co., Ltd., China
GASE: Gaussian Splatting-Based Automated System for Reconstructing Embodied-Simulation Environments
Jun 16, 2026Jiawei Zhang, Yiming Yan, Chao Liang +83D Gaussian Splatting 3D Reconstruction
AutoLab, SAI, Shanghai Jiao Tong University · AIM3 Lab, School of Information, Renmin University of China · Research Lab, Anyverse Dynamics
Local-GS: Accelerating 3D Gaussian Splatting via Tile-Local Warp Coherence
Jun 15, 2026Yang Luo, Yan Gong, Yongsheng Gao +33D Gaussian Splatting 3DGS Acceleration
State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin 150001, China · State Key Laboratory of Intelligent Green Vehicle and Mobility, the School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China · Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
RealityBridge: Bridging Editable 3D Gaussian Splatting Driving Simulations and Real-World Videos
Jun 15, 2026Zhenhua Wu, Yun Pang, Mingkun Chang +43D Gaussian Splatting Sim-to-Real Transfer
1Sun Yat-sen University · 2Shanghai Innovation Institute · 3Shenzhen Loop Area Institute
TurboGS: Accelerating 3D Gaussian Splatting via Error-Guided Sparse Pixel Sampling and Optimization
Jun 14, 2026Zheng Dong, Daifei Qiu, Pinxuan Dai +53D Gaussian Splatting 3DGS Acceleration
Zhejiang Sci-Tech University · Malanshan Audio&Video Laboratory · CAD&CG Laboratory, Zhejiang University +2
EmoZone-Talker: Regional Semantic Control of Audio-Driven 3DGS Talking Heads via Facial Action Units
Jun 14, 2026Tingting Chen, Shaojun Wang, Huaye Zhang +23D Gaussian Splatting Audio-Driven Facial Animation
China University of Petroleum (East China)
SpatialAvatar-0: High-Quality 4D Head Avatar with Multi-Stage Reconstruction
Jun 14, 2026Yiran Wang, Zeyu Zhang, Yuanming Li +23D Gaussian Splatting 4D Reconstruction
1USYD · 2SpatialReal · 3ZJU +1
MooMIns -- Monocular 3D Reconstruction and Object Pose Estimation from Multiple Instances
Jun 12, 2026Robert Langendörfer, Markus Hillemann, Markus Ulrich3D Gaussian Splatting 3D Reconstruction
Institute of Photogrammetry and Remote Sensing Karlsruhe Institute of Technology Karlsruhe, GER
SplatlessDF: Continuous Distance Field Mapping with Non-Splatting Gaussians
Jun 12, 2026Monisha Mushtary Uttsha, Lan Wu, Teresa Vidal-Calleja3D Gaussian Splatting 3D Reconstruction
UTS Robotics Institute, Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW 2007, Australia · School of Engineering, University of Western Australia, Perth, WA 6009, Australia
MoVerse: Real-Time Video World Modeling with Panoramic Gaussian Scaffold
Jun 11, 2026Yang Zhou, Ziheng Wang, Yuqin Lu +43D Gaussian Splatting Camera-Conditioned Video Generation
South China University of Technology · Orange Team, Moku Lab, HUJING Digital Media & Entertainment Group · Columbia University +1
Wild3R: Feed-Forward 3D Gaussian Splatting from Unconstrained Sparse Photo Collection
Jun 10, 2026Yuto Furutani, Takashi Otonari, Kaede Shiohara +13D Gaussian Splatting Sparse-View 3D Reconstruction
The University of Tokyo
Scene-Adaptive Nonlinear Tone Curves for Pseudo Ground-Truth Generation in Low-Light 3D Gaussian Splatting
Jun 10, 2026Mingzhe Lyu, Jinqiang Cui, Hong Zhang3D Gaussian Splatting Image Enhancement
Shenzhen Key Laboratory of Robotics and Computer Vision, Southern University of Science and Technology, Shenzhen, 518055, China. · Pengcheng Laboratory, Shenzhen, 518108, China.
Seeing What Matters: Perceptual Wrapper with Common Randomness for 3D Gaussian Splatting
Jun 10, 2026He-Bi Yang, Jing-Zhong Chen, Yen-Kuan Ho +53DGS Compression 3D Gaussian Splatting
National Yang Ming Chiao Tung University, Taiwan · National Chung Cheng University, Taiwan
A Scalable PyTorch Abstraction for Multi-GPU Gaussian Splatting
Jun 9, 2026Matthew Cong, Francis Williams, Jonathan Swartz +33D Gaussian Splatting GPU Acceleration
1NVIDIA · University of Toronto · 3Vector Institute
TRON: Tracing Rays to Orchestrate a Neural Renderer for 3D Gaussian Reconstructions
Jun 9, 2026Or Perel, Hassan Abu Alhaija, Zian Wang +43D Gaussian Splatting 3D Scene Editing
1NVIDIA · University of Toronto · 3Vector Institute
Envision4D: Envisioning Visual Futures via Feed-forward 4D Gaussian Splatting for Autonomous Driving
Jun 9, 2026Qi Song, Yifei He, Chi Zhang +63D Gaussian Splatting Autonomous Driving
Tsinghua University · The Chinese University of Hong Kong, Shenzhen
ManiSplat: Manipulation Trajectory Synthesis from Monocular Video via Decoupled 3D Gaussian Splatting
Jun 9, 2026Wenhao Hu, Haonan Zhou, Liu Liu +53D Gaussian Splatting Monocular 3D Reconstruction
1Zhejiang University · 2Horizon Robotics
GaussTrace: Provenance Analysis of 3D Gaussian Splatting Models with Evidence-based LLM Reasoning
Jun 9, 2026Haoliang Han, Ziyuan Luo, Renjie Wan3D Gaussian Splatting Data Provenance
Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China.
ABot-Earth 0.5: Generative 3D Earth Model
Jun 8, 2026Ming Qian, Tianjian Ouyang, Mingchao Sun +253D Gaussian Splatting 3D Reconstruction
AMAP CV Lab Alibaba Group
REFINE: Super-efficient 3D Gaussian Splatting Pruning via Rendering-Free Primitive Importance
Jun 8, 2026Zhang Chen, Shuai Wan, Mengting Yu +23D Gaussian Splatting 3D Gaussian Pruning
School of Electronics and Information, Northwestern Polytechnical University · School of Telecommunication Engineering, Xidian University · Department of Computer Science, City University of Hong Kong
Leveraging NeRF-Rendered Images for 3D Gaussian Splatting
Jun 8, 2026Mizuki Morikawa, Yuta Shimizu, Chunyu Li +23D Gaussian Splatting Novel View Synthesis
Institute of Science Tokyo, Tokyo, Japan
LEGS: Laplacian-Enhanced Gaussian Splatting with a Nonlinear Weighted Loss
Jun 6, 2026Yongfei Guo, Qizhou Huo, Xuan Sun +13D Gaussian Splatting 3D Reconstruction
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, China · University of Chinese Academy of Sciences, Beijing, China
UniSHARP: Universal Sharp Monocular View Synthesis
Jun 5, 2026Meixi Song, Dizhe Zhang, Hao Ren +43D Gaussian Splatting Neural Rendering
Insta360 Research · Sun Yat-sen University · Beihang University +2

Jun 18, 2026J/K move · Enter open · S save

From Uncertainty to Stability and Fidelity: Guiding Sparse-View 3D Gaussian Splatting with Fisher Information

Junbao Zhou, Qingshan Xu, Yuan Zhou, Xiaolong Shen+6

Nanyang Technological University, Singapore · University of Science and Technology of China, China · Zhejiang University, China+1

3D Gaussian Splatting Novel View Synthesis Sparse-View 3D Reconstruction

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3D Gaussian Splatting (3DGS) has emerged as a promising technique for novel view synthesis. However, 3DGS requires dense input views to achieve high-quality rendering. In sparse-view scenarios, 3DGS often prones to overfitting, resulting in noticeable artifacts and degraded rendering quality. Previous methods explore to address this issue by introducing additional priors (e.g. depth priors) or integrating regularization techniques (e.g. Dropout). However, these methods are often applied without principled guidance. In particular, prior-based augmentation typically samples novel viewpoints randomly, while Dropout-based regularization randomly removes Gaussians. The compounded randomness introduces uncertainty and instability, limiting the fidelity of novel view synthesis. In this paper, we propose a novel method for sparse-view 3DGS that incorporates Fisher Information to quantitatively guide the utilization of geometric priors and regularization. Specifically, our method comprises two key components: (1) Stereo augmentation with Fisher Information. By leveraging Fisher Information, we actively select most informative supporting views and use depth priors to curate reliable pseudo ground truths, which reduces randomness in augmentation and improves stability and rendering fidelity; (2) Uncertainty-aware regularization. We reduce the instability of Dropout-based regularization by using Fisher Information to quantitatively measure the uncertainty of each 3D Gaussian, and adaptively adjust the removal probability, leading to more stable and effective regularization. With these two components, our method effectively mitigates overfitting and improves the stability of optimization in sparse-view 3DGS, resulting in superior rendering fidelity. Extensive experiments show that our method achieves state-of-the-art performance in sparse-view novel view synthesis benchmarks.