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

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.5Download Report PDF
3D Gaussian Splattinglarge-scale scene reconstructionsignal structure recovery
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 the scene frequency. To address this, we reframe 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 with a substantial margin in both efficiency and rendering quality in large-scale scenes.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes SIG, a scheduler that synchronizes image supervision with Gaussian frequencies during large-scale 3D Gaussian Splatting reconstruction. According to the taxonomy, this work occupies a unique position within the 'Signal Structure and Frequency-Aware Optimization' leaf under 'Holistic and Hierarchical Optimization Frameworks'. Notably, this leaf contains only the original paper itself—no sibling papers exist in this category. This isolation suggests the frequency-aware scheduling perspective represents a relatively unexplored direction within the broader holistic optimization landscape, which itself contains only six papers across three leaves.

The taxonomy reveals that neighboring research directions focus on different optimization strategies: 'Holistic Scene Modeling Without Partitioning' (two papers) addresses view-aware representations, while 'Hierarchical and Multi-Scale Representations' (two papers) employs pyramidal structures. The broader 'Holistic and Hierarchical Optimization Frameworks' branch sits alongside 'Spatial Partitioning' methods (six papers across two leaves) and 'Generalizable Feed-Forward Reconstruction' (seven papers across four leaves). The paper's signal-theoretic framing—deriving average sampling frequency and bandwidth—diverges from these neighboring approaches, which primarily tackle scalability through spatial decomposition or rapid feed-forward prediction rather than frequency-aware training modulation.

Among sixteen candidates examined across three contributions, none were found to clearly refute the proposed work. The 'SIG scheduler' contribution examined five candidates with zero refutations; the 'mathematical derivation of average frequency' examined five candidates with zero refutations; and 'Sphere-Constrained Gaussians' examined six candidates with zero refutations. This limited search scope—sixteen papers from semantic matching—suggests the frequency-aware scheduling concept has minimal direct overlap with examined prior work. However, the absence of sibling papers and the small candidate pool indicate this assessment reflects a narrow literature window rather than exhaustive coverage of related optimization strategies.

Based on the top-sixteen semantic matches and the taxonomy structure, the frequency-aware scheduling approach appears to occupy a sparse research direction within large-scale Gaussian Splatting. The single-paper leaf and zero refutations across contributions suggest novelty within the examined scope, though the limited search scale leaves open the possibility of relevant work in adjacent optimization or signal processing literature not captured by this analysis. The work's positioning at the intersection of signal theory and 3D reconstruction may explain both its taxonomic isolation and the lack of direct prior work in the candidate set.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
16
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: large-scale scene reconstruction using 3D Gaussian splatting. The field has rapidly diversified into several major branches addressing distinct challenges. Spatial partitioning and distributed training methods tackle computational scalability by dividing massive environments into manageable chunks, as seen in VastGaussian[1] and CityGaussian[40]. Holistic and hierarchical optimization frameworks focus on improving reconstruction quality through better training strategies and signal-aware techniques, while generalizable feed-forward reconstruction approaches like GS-LRM[2] and pixelSplat[38] aim to predict scene representations from limited views without per-scene optimization. Domain-specific branches address specialized environments—underwater scenes, aerial imagery, adverse weather, and urban settings—each requiring tailored handling of unique visual phenomena. Meanwhile, sparse-view reconstruction, surface extraction methods like SuGaR[27], and dynamic scene modeling extend the core technology to handle data-limited and time-varying scenarios. Active reconstruction and registration methods round out the taxonomy by addressing sensor planning and multi-scan alignment. Within the holistic optimization branch, a handful of works explore how to better structure the training process itself, moving beyond naive gradient descent. Signal Structure-Aware[0] emphasizes frequency-aware optimization strategies that respect the underlying signal characteristics of scene content, aiming for more stable convergence and higher fidelity in complex large-scale settings. This contrasts with approaches like AutoSplat[3], which automates hyperparameter tuning across diverse scenes, or HDRSplat[5], which targets high dynamic range capture. While many studies in this branch pursue hierarchical representations or coarse-to-fine refinement, Signal Structure-Aware[0] distinguishes itself by explicitly modeling how different frequency components should be treated during optimization, offering a complementary perspective on achieving robust reconstruction quality when scaling to expansive environments.

Claimed Contributions

Signal Structure-Aware Gaussian Splatting (SIG) scheduler

The authors introduce a scheduler that adaptively adjusts training image resolution and Gaussian densification based on scene frequency convergence, rather than using hard-coded schedules. This enables frequency-consistent optimization by synchronizing supervision signals with the evolving frequency content of 3D Gaussian representations.

5 retrieved papers
Mathematical derivation of average frequency for 3D Gaussians

The authors derive formal definitions for the average sampling frequency and scene signal bandwidth of 3D Gaussian representations. This theoretical framework characterizes the frequency content of Gaussian primitives and guides the adaptive scheduling strategy.

5 retrieved papers
Sphere-Constrained Gaussians

The authors introduce a method that constrains Gaussian primitives within spherical regions based on spatial priors from initialized point clouds. This approach reduces redundancy and prevents floater artifacts by limiting the optimization space while preserving scene structure.

6 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Signal Structure-Aware Gaussian Splatting (SIG) scheduler

The authors introduce a scheduler that adaptively adjusts training image resolution and Gaussian densification based on scene frequency convergence, rather than using hard-coded schedules. This enables frequency-consistent optimization by synchronizing supervision signals with the evolving frequency content of 3D Gaussian representations.

Contribution

Mathematical derivation of average frequency for 3D Gaussians

The authors derive formal definitions for the average sampling frequency and scene signal bandwidth of 3D Gaussian representations. This theoretical framework characterizes the frequency content of Gaussian primitives and guides the adaptive scheduling strategy.

Contribution

Sphere-Constrained Gaussians

The authors introduce a method that constrains Gaussian primitives within spherical regions based on spatial priors from initialized point clouds. This approach reduces redundancy and prevents floater artifacts by limiting the optimization space while preserving scene structure.

Table of Contents