Adaptive Gaussian Densification

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3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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You Only Gaussian Once: Controllable 3D Gaussian Splatting for Ultra-Densely Sampled Scenes
Apr 23, 2026Jinrang Jia, Zhenjia Li, Yifeng Shi3D Gaussian Splatting 3D Reconstruction
KE Holdings Inc., Beijing, China
Gaussians on a Diet: High-Quality Memory-Bounded 3D Gaussian Splatting Training
Apr 21, 2026Yangming Zhang, Jian Xu, Chaojian Li +73D Gaussian Splatting 3D Gaussian Pruning
Dept. of Computer Science University of Texas at Arlington · School of Computer Science Georgia Institute of Technology · School of Computing University of Georgia +2
Incoherent Deformation, Not Capacity: Diagnosing and Mitigating Overfitting in Dynamic Gaussian Splatting
Apr 17, 2026Ahmad Droby3D Gaussian Splatting Adaptive Gaussian Densification
Independent Researcher
Neural Gabor Splatting: Enhanced Gaussian Splatting with Neural Gabor for High-frequency Surface Reconstruction
Apr 17, 2026Haato Watanabe, Nobuyuki Umetani3D Gaussian Splatting 3D Reconstruction
The University of Tokyo
F4Splat: Feed-Forward Predictive Densification for Feed-Forward 3D Gaussian Splatting
Mar 22, 2026Injae Kim, Chaehyeon Kim, Minseong Bae +23D Gaussian Splatting Adaptive Gaussian Densification
KAIST, Daejeon, Republic of Korea · Korea University, Seoul, Republic of Korea
Proxy-GS: Unified Occlusion Priors for Training and Inference in Structured 3D Gaussian Splatting
Sep 29, 2025Yuanyuan Gao, Yuning Gong, Yifei Liu +63D Gaussian Splatting 3DGS Acceleration
Shanghai Artificial Intelligence Laboratory · Northwestern Polytechnical University · Sichuan University +2
Effective Gaussian Management for High-fidelity Scene Reconstruction
Sep 16, 2025Jiateng Liu, Hao Gao, Jiu-Cheng Xie +53D Gaussian Splatting 3D Reconstruction
School of Automation, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China · Department of Computer and Information Science, University of Macau, Taipa, Macau · school of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China +2
MVGS: Multi-view Regulated Gaussian Splatting for Novel View Synthesis
Oct 2, 2024Xiaobiao Du, Yida Wang, Xin YuMulti-View Consistency 3D Gaussian Splatting
University of Technology Sydney · Li Auto Inc. · Australian Institute for Machine Learning, Adelaide University

Apr 23, 2026, cs.CVJ/K move · Enter open · S save

You Only Gaussian Once: Controllable 3D Gaussian Splatting for Ultra-Densely Sampled Scenes

Jinrang Jia, Zhenjia Li, Yifeng Shi

KE Holdings Inc., Beijing, China

3D Gaussian Splatting 3D Reconstruction Adaptive Gaussian Densification

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3D Gaussian Splatting (3DGS) has revolutionized neural rendering, yet existing methods remain predominantly research prototypes ill-suited for production-level deployment. We identify a critical "Industry-Academia Gap" hindering real-world application: unpredictable resource consumption from heuristic Gaussian growth, the "sparsity shield" of current benchmarks that rewards hallucination over physical fidelity, and severe multi-sensor data pollution. To bridge this gap, we propose YOGO (You Only Gaussian Once), a system-level framework that reformulates the stochastic growth process into a deterministic, budget-aware equilibrium. YOGO integrates a novel budget controller for hardware-constrained resource allocation and an availability-registration protocol for robust multi-sensor fusion. To push the boundaries of reconstruction fidelity, we introduce Immersion v1.0, the first ultra-dense indoor dataset specifically designed to break the "sparsity shield." By providing saturated viewpoint coverage, Immersion v1.0 forces algorithms to focus on extreme physical fidelity rather than viewpoint interpolation, and enables the community to focus on the upper limits of high-fidelity reconstruction. Extensive experiments demonstrate that YOGO achieves state-of-the-art visual quality while maintaining a strictly deterministic profile, establishing a new standard for production-grade 3DGS. To facilitate reproducibility, part scenes of Immersion v1.0 dataset and source code of YOGO has been publicly released. The project link is https://jjrcn.github.io/yogo-project-home/.

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