Signal Structure-Aware Gaussian Splatting for Large-Scale Scene Reconstruction

• URL: http://arxiv.org/abs/2607.01698v2
• Date: Mon, 06 Jul 2026 17:22:05 GMT
• Title: Signal Structure-Aware Gaussian Splatting for Large-Scale Scene Reconstruction
• Abstract summary: We propose SIG, a novel scheduler that synchronizes image supervision with Gaussian frequencies.<n>Specifically, we derive the average sampling frequency and bandwidth of 3D representations, and then regulate the training image resolution and the Gaussian densification process.<n>Our framework enables frequency-consistent, geometry-aware, and floater-free training, achieving state-of-the-art performance by a substantial margin in both efficiency and rendering quality.
• Score: 25.32043603138213
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 3D Gaussian Splatting has demonstrated remarkable potential in novel view synthesis. In contrast to small-scale scenes, large-scale scenes inevitably contain sparsely observed regions with excessively sparse initial points. In this case, supervising Gaussians initialized from low-frequency sparse points with high-frequency images often induces uncontrolled densification and redundant primitives, degrading both efficiency and quality. Intuitively, this issue can be mitigated with scheduling strategies, which can be categorized into two paradigms: modulating target signal frequency via densification and modulating sampling frequency via image resolution. However, previous scheduling strategies are primarily hardcoded, failing to perceive the convergence behavior of scene frequency. To address this, we reframe the scene reconstruction problem from the perspective of signal structure recovery and propose SIG, a novel scheduler that synchronizes image supervision with Gaussian frequencies. Specifically, we derive the average sampling frequency and bandwidth of 3D representations, and then regulate the training image resolution and the Gaussian densification process based on scene frequency convergence. Furthermore, we introduce Sphere-Constrained Gaussians, which leverage the spatial prior of initialized point clouds to control Gaussian optimization. Our framework enables frequency-consistent, geometry-aware, and floater-free training, achieving state-of-the-art performance by a substantial margin in both efficiency and rendering quality in large-scale scenes. The code is available at: https://github.com/weiyixue999/Signal_Structure_Aware_Gaussian

Related papers

• Fi-Gaussian: Frequency-Aware Implicit Gaussian Splatting for Single Image Dehazing [25.472500911155347]
  We propose Fi-Gaussian, a frequency-aware implicit Gaussian splatting network for single image dehazing.<n>Fi-Gaussian achieves state-of-the-art quantitative performance and produces visually superior dehazed results.
  arXiv  Detail & Related papers  (2026-06-15T03:31:41Z)
• Faster 3D Gaussian Splatting Convergence via Structure-Aware Densification [52.517763621139956]
  3D Gaussian Splatting has emerged as a powerful scene representation for real-time novel-view synthesis.<n>Standard adaptive density control relies on screen-space positional gradients.<n>We present a structure-aware densification framework for 3D Gaussian Splatting.
  arXiv  Detail & Related papers  (2026-04-30T15:37:20Z)
• DenoiseGS: Gaussian Reconstruction Model for Burst Denoising [29.507111067242118]
  We propose DenoiseGS, the first framework to leverage the efficiency of 3D Gaussian Splatting for burst denoising.<n>Our approach addresses two key challenges when applying feedforward Gaussian reconsturction model to noisy inputs.<n>Experiments demonstrate that DenoiseGS significantly exceeds the state-of-the-art NeRF-based methods on both burst denoising and novel view synthesis.
  arXiv  Detail & Related papers  (2025-11-28T07:29:54Z)
• Improving Accuracy and Efficiency of Implicit Neural Representations: Making SIREN a WINNER [0.0]
  We identify and address a fundamental limitation of sinusoidal representation networks (SIRENs)<n>In extreme cases, when the network's frequency support misaligns with the target spectrum, a'spectral bottleneck' is observed.<n>We propose WINNER - Weight Initialization with Noise for Neural Representations.
  arXiv  Detail & Related papers  (2025-09-16T11:41:13Z)
• RobustSplat: Decoupling Densification and Dynamics for Transient-Free 3DGS [79.15416002879239]
  3D Gaussian Splatting has gained significant attention for its real-time, photo-realistic rendering in novel-view synthesis and 3D modeling.<n>Existing methods struggle with accurately modeling scenes affected by transient objects, leading to artifacts in the rendered images.<n>We propose RobustSplat, a robust solution based on two critical designs.
  arXiv  Detail & Related papers  (2025-06-03T11:13:48Z)
• DGTR: Distributed Gaussian Turbo-Reconstruction for Sparse-View Vast Scenes [81.56206845824572]
  Novel-view synthesis (NVS) approaches play a critical role in vast scene reconstruction. Few-shot methods often struggle with poor reconstruction quality in vast environments. This paper presents DGTR, a novel distributed framework for efficient Gaussian reconstruction for sparse-view vast scenes.
  arXiv  Detail & Related papers  (2024-11-19T07:51:44Z)
• CityGaussianV2: Efficient and Geometrically Accurate Reconstruction for Large-Scale Scenes [53.107474952492396]
  CityGaussianV2 is a novel approach for large-scale scene reconstruction.<n>We implement a decomposed-gradient-based densification and depth regression technique to eliminate blurry artifacts and accelerate convergence.<n>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)
• MCGS: Multiview Consistency Enhancement for Sparse-View 3D Gaussian Radiance Fields [100.90743697473232]
  Radiance fields represented by 3D Gaussians excel at synthesizing novel views, offering both high training efficiency and fast rendering.<n>Existing methods often incorporate depth priors from dense estimation networks but overlook the inherent multi-view consistency in input images.<n>We propose a view synthesis framework based on 3D Gaussian Splatting, enabling scene reconstruction from sparse views.
  arXiv  Detail & Related papers  (2024-10-15T08:39:05Z)
• CoherentGS: Sparse Novel View Synthesis with Coherent 3D Gaussians [18.42203035154126]
  We introduce a structured Gaussian representation that can be controlled in 2D image space.<n>We then constraint the Gaussians, in particular their position, and prevent them from moving independently during optimization.<n>We demonstrate significant improvements compared to the state-of-the-art sparse-view NeRF-based approaches on a variety of scenes.
  arXiv  Detail & Related papers  (2024-03-28T15:27:13Z)
• FreGS: 3D Gaussian Splatting with Progressive Frequency Regularization [67.47895278233717]
  We develop a progressive frequency regularization technique to tackle the over-reconstruction issue within the frequency space. FreGS achieves superior novel view synthesis and outperforms the state-of-the-art consistently.
  arXiv  Detail & Related papers  (2024-03-11T17:00:27Z)
• Two-Stage Monte Carlo Denoising with Adaptive Sampling and Kernel Pool [4.194950860992213]
  We tackle the problems in Monte Carlo rendering by proposing a two-stage denoiser based on the adaptive sampling strategy. In the first stage, concurrent to adjusting samples per pixel (spp) on-the-fly, we reuse the computations to generate extra denoising kernels applying on the adaptively rendered image. In the second stage, we design the position-aware pooling and semantic alignment operators to improve spatial-temporal stability.
  arXiv  Detail & Related papers  (2021-03-30T07:05:55Z)

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.