Gradient-Direction-Aware Density Control for 3D Gaussian Splatting
Not on the map. Why?
Abstract (arXiv)
The emergence of 3D Gaussian Splatting (3DGS) has significantly advanced Novel View Synthesis (NVS) through explicit scene representation, enabling real-time photorealistic rendering. However, existing approaches manifest two critical limitations in complex scenarios: (1) Over-reconstruction occurs when persistent large Gaussians cannot meet adaptive splitting thresholds during density control. This is exacerbated by conflicting gradient directions that prevent effective splitting of these Gaussians; (2) Over-densification of Gaussians occurs in regions with aligned gradient aggregation, leading to redundant component proliferation. This redundancy significantly increases memory overhead due to unnecessary data retention. We present Gradient-Direction-Aware Gaussian Splatting (GDAGS) to address these challenges. Our key innovations: the Gradient Coherence Ratio (GCR), computed through normalized gradient vector norms, which explicitly discriminates Gaussians with concordant versus conflicting gradient directions; and a nonlinear dynamic weighting mechanism leverages the GCR to enable gradient-direction-aware density control. Specifically, GDAGS prioritizes conflicting-gradient Gaussians during splitting operations to enhance geometric details while suppressing redundant concordant-direction Gaussians. Conversely, in cloning processes, GDAGS promotes concordant-direction Gaussian densification for structural completion while preventing conflicting-direction Gaussian overpopulation. Comprehensive evaluations across diverse real-world benchmarks demonstrate that GDAGS achieves superior rendering quality while effectively mitigating over-reconstruction, suppressing over-densification, and constructing compact scene representations.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 26 of its 35 references that are in the map. First posted on arXiv 12 Aug 2025.
Builds on
26 of 35 references.
References · 26
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
- 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
- 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,698
- 2021Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fieldscited by 2,013
- 2021Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fieldscited by 1,957
- 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,607
- 20242D Gaussian Splatting for Geometrically Accurate Radiance Fieldscited by 1,061
- 2021Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstructioncited by 961
- 2023Mip-Splatting: Alias-free 3D Gaussian Splattingcited by 661
- 2021Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fieldscited by 569
- 2023Compact 3D Gaussian Representation for Radiance Fieldcited by 365
- 2024Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenescited by 226
14 more
- 20243D Gaussian Splatting as Markov Chain Monte Carlocited by 210
- 2024Mini-Splatting: Representing Scenes with a Constrained Number of Gaussianscited by 208
- 2024GaussianPro: 3D Gaussian Splatting with Progressive Propagationcited by 199
- 2024Taming 3DGS: High-Quality Radiance Fields with Limited Resourcescited by 198
- 2024Pixel-GS: Density Control with Pixel-aware Gradient for 3D Gaussian Splattingcited by 111
- 2024RaDe-GS: Rasterizing Depth in Gaussian Splattingcited by 110
- 2024FreGS: 3D Gaussian Splatting with Progressive Frequency Regularizationcited by 103
- 2024GES: Generalized Exponential Splatting for Efficient Radiance Field Renderingcited by 92
- 2024AbsGS: Recovering Fine Details for 3D Gaussian Splattingcited by 80
- 20243D-HGS: 3D Half-Gaussian Splattingcited by 43
- 2025Frequency-Aware Density Control via Reparameterization for High-Quality Rendering of 3D Gaussian Splattingcited by 8
- 2025HoGS: Unified Near and Far Object Reconstruction via Homogeneous Gaussian Splattingcited by 4
- 2025Decomposing Densification in Gaussian Splatting for Faster 3D Scene Reconstructioncited by 3
- 2025PSRGS:Progressive Spectral Residual of 3D Gaussian for High-Frequency Recoverycited by 2
Built on by
Citing papers per year: 2025: 2; 2026: 5.
7 papers.
- 2025ConeGS: Error-Guided Densification Using Pixel Cones for Improved Reconstruction With Fewer Primitivescited by 5
- 2025STAvatar: Soft Binding and Temporal Density Control for Monocular 3D Head Avatars Reconstructioncited by 3
- 2026FlowObject: Flow Steering for Bridging Generative Priors and Reconstruction Fidelitycited by 1
- 2026Incoherent Deformation, Not Capacity: Diagnosing and Mitigating Overfitting in Dynamic Gaussian Splattingcited by 0
- 2026LEGS: Laplacian-Enhanced Gaussian Splatting with a Nonlinear Weighted Losscited by 0
- 2026Exploration Matters for Escaping the Blur Trap in 3D Gaussian Splattingcited by 0
- 2026Gauss What You Need: Compact Gaussian Splatting Across Scene Scalescited by 0
- 2026Gauss What You Need: Compact Gaussian Splatting Across Scene Scales25 Sep 2026
- 2026Exploration Matters for Escaping the Blur Trap in 3D Gaussian Splatting20 Jul 2026
- 2026FlowObject: Flow Steering for Bridging Generative Priors and Reconstruction Fidelity17 Jun 2026
- 2026LEGS: Laplacian-Enhanced Gaussian Splatting with a Nonlinear Weighted Loss6 Jun 2026
- 2026Incoherent Deformation, Not Capacity: Diagnosing and Mitigating Overfitting in Dynamic Gaussian Splatting17 Apr 2026
- 2025STAvatar: Soft Binding and Temporal Density Control for Monocular 3D Head Avatars Reconstruction25 Nov 2025
- 2025ConeGS: Error-Guided Densification Using Pixel Cones for Improved Reconstruction With Fewer Primitives10 Nov 2025