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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FRUC: Feedforward Dynamic Scene Reconstruction from Uncalibrated Collaborative Driving Views
May 28, 2026Yihang Tao, Yu Guo, Zhengru Fang +23D Gaussian Splatting 3D Reconstruction
Hong Kong JC STEM Lab of Smart City, City University of Hong Kong
Smaller and Faster 3DGS via Post-Training Dictionary Learning
May 28, 2026Jiarong Gong, Jonas Unger, Ehsan Miandji3DGS Compression Model Compression
Linköping University Department of Science and technology
DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding
May 28, 2026Luzhou Ge, Xiangyu Zhu, Jinyan Liu +13D Gaussian Splatting Open-Vocabulary 3D Segmentation
School of Computer Science, Beijing Institute of Technology, Beijing 100081, China.
BitC-3DGS: High-Capacity 3D Gaussian Splatting Watermarking via Bit Compression
May 28, 2026Yuquan Bi, Baosheng Yu, Yingke Lei +53D Gaussian Splatting Robust Watermarking
School of Cyber Science and Engineering, Southeast University, Nanjing, China · Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore · College of Electronic Engineer, National University of Defense and Technology, Hefei, 230000, China +5
Learning Representations from 3D Gaussian Splats
May 28, 2026Julia Farganus, Krzysztof Żurawicki, Arkadiusz Gaweł +23D Gaussian Splatting Geometric Representation Learning
Department of Artificial Intelligence, Wrocław University of Science and Technology, Wrocław, Poland
Comparative evaluation of photogrammetric reconstruction methods and 3D Gaussian Splatting for road surface roughness analysis
May 28, 2026Marouane Elmegdar, Teng Xiao3D Gaussian Splatting Multi-View 3D Reconstruction
School of Computer Science, Hubei University of Technology, Wuhan, China 430068
Eulerian Gaussian Splatting using Hashed Probability Pyramids
May 27, 2026Mia Gaia Polansky, George Kopanas, Stephan Garbin +23D Gaussian Splatting Neural Rendering
Harvard University · Google DeepMind · Google
Deformable Gaussian Occupancy: Decoupling Rigid and Nonrigid Motion with Factorized Distillation
May 27, 2026Yang Gao, Wuyang Li, Po-Chien Luan +13D Gaussian Splatting Autonomous Driving Perception
´Ecole Polytechnique F´ed´erale de Lausanne (EPFL), Switzerland
DelowlightSplat: Feed-Forward Gaussian Splatting for Lowlight 3D Scene Reconstruction
May 26, 2026Fuzhen Jiang, Zengtian Xie, Zhuoran Li3D Gaussian Splatting 3D Reconstruction
Hangzhou Dianzi University · Hangzhou, China · Zhuhai College of Science and Technology +1
Gaussian-Voxel Duet: A Dual-Scaffolding Hybrid Representation for Fast and Accurate Monocular Surface Reconstruction
May 26, 2026Zhenhua Du, Zhen Tan, Haoyu Zhang +33D Gaussian Splatting 3D Reconstruction
Zhejiang University · Westlake University · National University of Defense Technology
TrackRef3D: Multi-View Consistent Track-then-Label for Open-World Referring Segmentation in 3D Gaussian Splatting
May 26, 2026Yuyang Tan, Renhe Zhang, Hang Zhang +2Multi-View Consistency 3D Gaussian Splatting
East China Normal University, Shanghai, China · Shanghai AI Laboratory · University of Electronic Science and Technology of China, Chengdu, China
Underwater360: Reconstructing Underwater Scenes from Panoramic Images with Omnidirectional Gaussian Splatting
May 26, 2026Jiangbei Hu, Weichao Song, Shibo Yu +83D Gaussian Splatting Underwater Imaging
School of Software, Dalian University of Technology · College of Computing and Data Science, Nanyang Technological University
F-RNG: Feed-Forward Relightable Neural Gaussians
May 25, 2026Guangming Fu, Jiahui Fan, Jian Yang +23D Gaussian Splatting 3D Reconstruction
Nankai University, China · Nanjing University, China · NVIDIA, USA
Accelerated Spatio-Temporal Gaussian Rendering via Kinematic and Semantic Priors
May 25, 2026Denis Gridusov, Maxim Popov, Sergey Kolyubin3D Gaussian Splatting 4D Reconstruction
ITMO University, St. Petersburg, Russia
SplitAvatar: One-shot Head Avatar with Autoregressive Gaussian Splitting
May 25, 2026Hongzhe Liao, Chuhua Xian, Hongmin Cai +23D Gaussian Splatting Adaptive Gaussian Densification
South China University of Technology · University of Tokyo · The Hong Kong University of Science and Technology
CodecSplat: Ultra-Compact Latent Coding for Feed-Forward 3D Gaussian Splatting
May 25, 2026Pengpeng Yu, Runqing Jiang, Qi Zhang +33DGS Compression 3D Gaussian Splatting
Sun Yat-sen University, China · Pengcheng Laboratory, China · Peking University, China
Physics-Aware 3D Gaussian Editing for Driving Scene Generation
May 25, 2026Feng Zhou, Jian Zhang, Yuhang Sun +53D Gaussian Splatting 3D Scene Editing
School of Artificial Intelligence, Jilin University, Changchun, China. · China FAW Group Co., Ltd., Changchun, China. · National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun, China.
Depth Peeling for High-Fidelity Gaussian-Enhanced Surfel Rendering
May 25, 2026Keyang Ye, Hongzhi Wu, Kun Zhou3D Gaussian Splatting Differentiable Rendering
State Key Lab of CAD&CG, Zhejiang University · Hangzhou Research Institute of Holographic and AI Technology
Multi-view Consistent 3D Gaussian Head Avatars 'without' Multi-view Generation
May 24, 2026Aviral Chharia, Fernando De la TorreMulti-View Consistency 3D Gaussian Splatting
Carnegie Mellon University
ConFi-GS Confidence-Guided High-Frequency Injection for 3D Gaussian Splatting Super-Resolution
May 24, 2026Jiaxiang Li, Zongtan Zhou, Zhen Tan +2Multi-View Consistency 3D Gaussian Splatting
ArtSplat: Feed-Forward Articulated 3D Gaussian Splatting from Sparse Multi-State Uncalibrated Views
May 23, 2026Inseo Lee, Yoonji Kim, Eugene Sohn +4Articulated Object Reconstruction 3D Gaussian Splatting
1Seoul National University · 2Sogang University · 3NAVER AI Lab
COSY: Compositional 3DGS Synthesis for Disentangled Human Head Editing
May 22, 2026Florian Barthel, Shalini De Mello, Koki Nagano +33D Gaussian Splatting Disentangled Representation Learning
Fraunhofer Heinrich-Hertz Institute, Germany · Humboldt University Berlin, Germany · NVIDIA, USA
RiGS: Rigid-aware 4D Gaussian Splatting from a Single Monocular Video
May 22, 2026Chenyu Wu, Wanhua Li, Zhu-Tian Chen +13D Gaussian Splatting Monocular 3D Reconstruction
Harvard University · Zhejiang University · Nanyang Technological University +1
GlowGS: Generative Semantic Feature Learning for 3D Gaussian Splatting in Nighttime Glow Scenes
May 22, 2026Beibei Lin, Xiao Cao, Jingyuan Guo +13D Gaussian Splatting 3D Reconstruction
National University of Singapore
LangFlash: Feed-forward 3D Language Gaussian Splatting from Sparse Unposed Images
May 22, 2026Yilong Liu, Wanhua Li, Chen Zhu-Tian +13D Gaussian Splatting Language-Embedded 3DGS
Harvard University · Tsinghua University · Nanyang Technological University +1
Diffusion-guided Generalizable Enhancer for Urban Scene Reconstruction
May 21, 2026Henry Che, Jingkang Wang, Yun Chen +33D Gaussian Splatting Diffusion Model Guidance
1Waabi · University of Illinois Urbana-Champaign · University of Toronto
No Pose, No Problem in 4D: Feed-Forward Dynamic Gaussians from Unposed Multi-View Videos
May 21, 2026Matteo Balice, Yanik Kunzi, Chenyangguang Zhang +33D Gaussian Splatting 4D Reconstruction
1Politecnico di Milano · 2ETH Zürich · 3ETH AI Center
TWINGS: Thin Plate Splines Warp-aligned Initialization for Sparse-View Gaussian Splatting
May 21, 2026Hyeseong Kim, Geonhui Son, Deukhee Lee +13D Gaussian Splatting Novel View Synthesis
Yonsei University · Korea Institute of Science and Technology
ForeSplat: Optimization-Aware Foresight for Feed-Forward 3D Gaussian Splatting
May 21, 2026Yuke Li, Weihang Liu, Cheng Zhang +83D Gaussian Splatting Meta-Learning
ShanghaiTech University · GGU Technology Co., Ltd · Stereye
Learning to Evolve: Multi-modal Interactive Fields for Robust Humanoid Navigation in Dynamic Environments
May 21, 2026Peifeng Jiang, Hong Liu, Jin Jin +23D Gaussian Splatting Safe Robot Navigation
State Key Laboratory of General Artificial Intelligence, Peking University, Shenzhen Graduate School · Oxford Robotics Institute, University of Oxford · Institute for Machine Learning, Department of Computer Science, ETH Zurich

May 28, 2026J/K move · Enter open · S save

FRUC: Feedforward Dynamic Scene Reconstruction from Uncalibrated Collaborative Driving Views

Yihang Tao, Yu Guo, Zhengru Fang, Haonan An+1

Hong Kong JC STEM Lab of Smart City, City University of Hong Kong

3D Gaussian Splatting 3D Reconstruction Multi-View 3D Reconstruction Dynamic Scene Reconstruction Feed-Forward 3D Reconstruction

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We present FRUC, a feed-forward 3D Gaussian splatting framework for dynamic scene reconstruction from uncalibrated collaborative driving views. Existing multi-agent reconstruction frameworks are often hindered by rigid prerequisites, demanding precise spatial calibration and slow per-scene optimization. In this paper, we rethink this task by conceptualizing a distributed multi-vehicle network as a spatio-temporally unstructured ego-centric multi-camera system, where the core challenge lies in enhancing ego-centric occluded geometry through collaboration without degrading the ego's accurately observed visible geometry, while preserving reconstruction efficiency. For efficient reconstruction, FRUC is built upon a visual grounded geometric Transformer backbone to enable one-shot, calibration-free inference from a flexible number of multi-vehicle views. To achieve non-destructive geometric supplementation under uncalibrated cross-agent misalignment, FRUC first introduces an ego-centric causal occlusion field that explicitly derives occlusion evolution as latent priors by modeling agent-wise spatio-temporal correlations. Guided by these occlusion priors, it further formulates cross-agent integration as a deterministic residual denoising process via zero-initialized injection, turning challenging cross-agent fusion into bounded residual learning for robust collaborative blind-spot completion. Through extensive evaluations on the real-world V2XReal and UrbanIng-V2X datasets, FRUC is shown to be a new state-of-the-art for the scene reconstruction of dynamic collaborative driving environments, significantly outperforming existing methods in both rendering quality and efficiency.