Frequency-Aware Density Control via Reparameterization for High-Quality Rendering of 3D Gaussian Splatting
- URL: http://arxiv.org/abs/2503.07000v1
- Date: Mon, 10 Mar 2025 07:30:45 GMT
- Title: Frequency-Aware Density Control via Reparameterization for High-Quality Rendering of 3D Gaussian Splatting
- Authors: Zhaojie Zeng, Yuesong Wang, Lili Ju, Tao Guan,
- Abstract summary: 3D Gaussian Splatting (3DGS) can better represent scene details in high-frequency regions.<n>3DGS currently lacks an explicit constraint linking the density and scale of 3D Gaussians across the domain.<n>We develop a frequency-aware density control strategy, consisting of densification and deletion, to improve representation quality.
- Score: 11.892334330536974
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: By adaptively controlling the density and generating more Gaussians in regions with high-frequency information, 3D Gaussian Splatting (3DGS) can better represent scene details. From the signal processing perspective, representing details usually needs more Gaussians with relatively smaller scales. However, 3DGS currently lacks an explicit constraint linking the density and scale of 3D Gaussians across the domain, leading to 3DGS using improper-scale Gaussians to express frequency information, resulting in the loss of accuracy. In this paper, we propose to establish a direct relation between density and scale through the reparameterization of the scaling parameters and ensure the consistency between them via explicit constraints (i.e., density responds well to changes in frequency). Furthermore, we develop a frequency-aware density control strategy, consisting of densification and deletion, to improve representation quality with fewer Gaussians. A dynamic threshold encourages densification in high-frequency regions, while a scale-based filter deletes Gaussians with improper scale. Experimental results on various datasets demonstrate that our method outperforms existing state-of-the-art methods quantitatively and qualitatively.
Related papers
- Improving Adaptive Density Control for 3D Gaussian Splatting [3.2248805768155835]
3D Gaussian Splatting is one of the most influential works in the past year.
It faces challenges to properly manage the number of Gaussian primitives that are used during scene reconstruction.
We propose three new improvements to the adaptive density control mechanism.
arXiv Detail & Related papers (2025-03-18T14:09:10Z) - ResGS: Residual Densification of 3D Gaussian for Efficient Detail Recovery [11.706262924395768]
3D-GS often struggles to capture rich details and complete geometry.<n>We introduce a novel densification method, residual split, which adds a downscaled Gaussian as a residual.<n>Our approach is capable of adaptively retrieving details and complementing missing geometry while enabling progressive refinement.
arXiv Detail & Related papers (2024-12-10T13:19:27Z) - Volumetrically Consistent 3D Gaussian Rasterization [18.84882580327324]
We show that splatting and its approximations are unnecessary, even within a viewizer.<n>We use this analytic transmittance framework to derive more physically-accurate alpha values than 3DGS.<n>Our method represents opaque surfaces with higher accuracy and fewer points than 3DGS.
arXiv Detail & Related papers (2024-12-04T15:05:43Z) - Beyond Gaussians: Fast and High-Fidelity 3D Splatting with Linear Kernels [51.08794269211701]
We introduce 3D Linear Splatting (3DLS), which replaces Gaussian kernels with linear kernels to achieve sharper and more precise results.<n>3DLS demonstrates state-of-the-art fidelity and accuracy, along with a 30% FPS improvement over baseline 3DGS.
arXiv Detail & Related papers (2024-11-19T11:59:54Z) - CityGaussianV2: Efficient and Geometrically Accurate Reconstruction for Large-Scale Scenes [53.107474952492396]
CityGaussianV2 is a novel approach for large-scale scene reconstruction.
We implement a decomposed-gradient-based densification and depth regression technique to eliminate blurry artifacts and accelerate convergence.
Our method strikes a promising balance between visual quality, geometric accuracy, as well as storage and training costs.
arXiv Detail & Related papers (2024-11-01T17:59:31Z) - Mipmap-GS: Let Gaussians Deform with Scale-specific Mipmap for Anti-aliasing Rendering [81.88246351984908]
We propose a unified optimization method to make Gaussians adaptive for arbitrary scales.
Inspired by the mipmap technique, we design pseudo ground-truth for the target scale and propose a scale-consistency guidance loss to inject scale information into 3D Gaussians.
Our method outperforms 3DGS in PSNR by an average of 9.25 dB for zoom-in and 10.40 dB for zoom-out.
arXiv Detail & Related papers (2024-08-12T16:49:22Z) - MVG-Splatting: Multi-View Guided Gaussian Splatting with Adaptive Quantile-Based Geometric Consistency Densification [8.099621725105857]
We introduce MVG-Splatting, a solution guided by Multi-View considerations.
We propose an adaptive quantile-based method that dynamically determines the level of additional densification.
This approach significantly enhances the overall fidelity and accuracy of the 3D reconstruction process.
arXiv Detail & Related papers (2024-07-16T15:24:01Z) - PUP 3D-GS: Principled Uncertainty Pruning for 3D Gaussian Splatting [59.277480452459315]
We propose a principled sensitivity pruning score that preserves visual fidelity and foreground details at significantly higher compression ratios.<n>We also propose a multi-round prune-refine pipeline that can be applied to any pretrained 3D-GS model without changing its training pipeline.
arXiv Detail & Related papers (2024-06-14T17:53:55Z) - EfficientGS: Streamlining Gaussian Splatting for Large-Scale High-Resolution Scene Representation [29.334665494061113]
'EfficientGS' is an advanced approach that optimize 3DGS for high-resolution, large-scale scenes.
We analyze the densification process in 3DGS and identify areas of Gaussian over-proliferation.
We propose a selective strategy, limiting Gaussian increase to key redundant primitives, thereby enhancing the representational efficiency.
arXiv Detail & Related papers (2024-04-19T10:32:30Z) - AbsGS: Recovering Fine Details for 3D Gaussian Splatting [10.458776364195796]
3D Gaussian Splatting (3D-GS) technique couples 3D primitives with differentiable Gaussianization to achieve high-quality novel view results.
However, 3D-GS frequently suffers from over-reconstruction issue in intricate scenes containing high-frequency details, leading to blurry rendered images.
We present a comprehensive analysis of the cause of aforementioned artifacts, namely gradient collision.
Our strategy efficiently identifies large Gaussians in over-reconstructed regions, and recovers fine details by splitting.
arXiv Detail & Related papers (2024-04-16T11:44:12Z) - GaussianPro: 3D Gaussian Splatting with Progressive Propagation [49.918797726059545]
3DGS relies heavily on the point cloud produced by Structure-from-Motion (SfM) techniques.
We propose a novel method that applies a progressive propagation strategy to guide the densification of the 3D Gaussians.
Our method significantly surpasses 3DGS on the dataset, exhibiting an improvement of 1.15dB in terms of PSNR.
arXiv Detail & Related papers (2024-02-22T16:00:20Z) - GES: Generalized Exponential Splatting for Efficient Radiance Field Rendering [112.16239342037714]
GES (Generalized Exponential Splatting) is a novel representation that employs Generalized Exponential Function (GEF) to model 3D scenes.
With the aid of a frequency-modulated loss, GES achieves competitive performance in novel-view synthesis benchmarks.
arXiv Detail & Related papers (2024-02-15T17:32:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.