OpenNovelty.org

A Step to Decouple Optimization in 3DGS

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
3DGSoptimizerregularization
Abstract:

3D Gaussian Splatting (3DGS) has emerged as a powerful technique for real-time novel view synthesis. As an explicit representation optimized through gradient propagation among primitives, optimization widely accepted in deep neural networks (DNNs) is actually adopted in 3DGS, such as synchronous weight updating and Adam with the adaptive gradient. However, considering the physical significance and specific design in 3DGS, there are two overlooked details in the optimization of 3DGS: (i) update step coupling, which induces optimizer state rescaling and costly attribute updates outside the viewpoints, and (ii) gradient coupling in the moment, which may lead to under- or over-effective regularization. Nevertheless, such a complex coupling is under-explored. After revisiting the optimization of 3DGS, we take a step to decouple it and recompose the process into: Sparse Adam, Re-State Regularization and Decoupled Attribute Regularization. Taking a large number of experiments under the 3DGS and 3DGS-MCMC frameworks, our work provides a deeper understanding of these components. Finally, based on the empirical analysis, we re-design the optimization and propose AdamW-GS by re-coupling the beneficial components, under which better optimization efficiency and representation effectiveness are achieved simultaneously.

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 a fundamental rethinking of the optimization process in 3D Gaussian Splatting by decoupling update step coupling and gradient coupling in the moment, then recomposing the process into Sparse Adam, Re-State Regularization, and Decoupled Attribute Regularization. It resides in the 'Gradient and Optimizer Modifications' leaf under 'Optimization Algorithms and Training Strategies'. Notably, this leaf contains only the original paper itself—no sibling papers were identified in the taxonomy. This suggests the work occupies a relatively sparse research direction focused specifically on redesigning the core optimizer mechanics rather than adding external constraints or architectural changes.

The taxonomy reveals that most optimization-focused work in 3DGS falls into neighboring categories: 'Multi-View Consistency Enforcement' addresses cross-view constraints during training, while 'Pose-Free and Unposed Reconstruction' jointly optimizes camera parameters. The broader 'Regularization and Constraints' branch contains numerous methods imposing geometric or perceptual priors without altering the optimizer itself. The paper's focus on gradient propagation and optimizer state management distinguishes it from these approaches, which typically accept standard Adam optimization and layer additional losses on top. The taxonomy's scope_note explicitly excludes methods using standard optimizers with external regularization, reinforcing that this work targets a different design axis.

Among thirty candidates examined through semantic search, none were found to clearly refute any of the three core contributions. For the decoupling and recomposition framework, ten candidates were examined with zero refutable matches. Similarly, the AdamW-GS optimizer and autonomous redundancy removal each had ten candidates examined, again with no clear prior work providing the same mechanisms. This limited search scope means the analysis captures top semantic neighbors but cannot claim exhaustive coverage of all optimizer-focused 3DGS literature. The absence of refutations among these thirty candidates suggests the specific combination of sparse Adam, re-state regularization, and decoupled attribute updates may be novel within the examined set.

Based on the limited search of thirty semantically similar papers, the work appears to introduce a distinct perspective on 3DGS optimization by targeting optimizer internals rather than external regularization or architectural extensions. The taxonomy structure confirms this direction is sparsely populated, with no identified siblings in the same leaf. However, the search scope remains constrained to top-K semantic matches and does not guarantee comprehensive coverage of all gradient-based or optimizer-focused methods in the broader 3DGS literature.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Optimization of 3D Gaussian Splatting for novel view synthesis. The field has evolved rapidly since the foundational 3D Gaussian Splatting[17] method, organizing into several major branches that address complementary challenges. Optimization Algorithms and Training Strategies refine the core learning process through gradient modifications and adaptive scheduling. Regularization and Constraints impose geometric or appearance priors to prevent overfitting and improve quality. Representation Enhancements extend the basic Gaussian primitives with richer attributes for complex lighting or dynamic scenes, as seen in works like Deferred Reflection[24] and Spacetime Feature[8]. Sparse-View and Few-Shot Synthesis tackles data-scarce regimes using priors or multi-view geometry, while Generalizable Feed-Forward Methods like Freesplat[15] and Mvgs[3] learn to predict Gaussians directly from input views without per-scene optimization. Compression and Efficiency focuses on reducing storage and rendering costs, exemplified by Compressed 3DGS[2] and Fast Feedforward Compression[44]. Specialized Rendering Scenarios handle challenging conditions such as low-light environments or deformable objects, and Foundational Methods and Surveys provide overarching perspectives on the rapidly growing literature. Within this landscape, a particularly active tension exists between per-scene optimization depth and cross-scene generalization speed. Decouple Optimization[0] sits squarely in the Optimization Algorithms branch, proposing gradient and optimizer modifications to improve training stability and convergence for individual scenes. This contrasts with generalizable approaches like Generalizable 3DGS[5] or PF3plat[4], which sacrifice some per-scene refinement for the ability to instantly synthesize novel views across diverse datasets. Meanwhile, methods addressing sparse inputs, such as GPS-Gaussian[19] or Sparse Viewpoint[21], often blend optimization strategies with strong regularization to compensate for limited observations. Decouple Optimization[0] emphasizes refining the core training loop itself, distinguishing it from works that add auxiliary losses or architectural components, and positioning it as a foundational improvement applicable across many downstream scenarios where per-scene quality remains paramount.

Claimed Contributions

Decoupling and recomposing 3DGS optimization into three components

The authors analyze the complex coupling in 3DGS optimization and decompose it into three distinct components: Sparse Adam for asynchronous updates, Re-State Regularization (RSR) for moment rescaling, and Decoupled Attribute Regularization (DAR) for gradient-decoupled regularization. This decomposition provides a deeper understanding of optimization mechanisms in 3DGS.

10 retrieved papers
AdamW-GS optimizer with controllable attribute regularization

The authors propose AdamW-GS, a redesigned optimizer that recouples the beneficial components identified through their decoupling analysis. This optimizer achieves better optimization efficiency and representation effectiveness simultaneously by enabling more controllable regularization of primitive attributes.

10 retrieved papers
Autonomous redundancy removal without additional pruning operations

The authors demonstrate that their AdamW-GS method can automatically remove redundant primitives in vanilla 3DGS through proper regularization alone, without requiring extra pruning components. This is achieved while maintaining or improving reconstruction quality and optimization efficiency.

10 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

Decoupling and recomposing 3DGS optimization into three components

The authors analyze the complex coupling in 3DGS optimization and decompose it into three distinct components: Sparse Adam for asynchronous updates, Re-State Regularization (RSR) for moment rescaling, and Decoupled Attribute Regularization (DAR) for gradient-decoupled regularization. This decomposition provides a deeper understanding of optimization mechanisms in 3DGS.

Contribution

AdamW-GS optimizer with controllable attribute regularization

The authors propose AdamW-GS, a redesigned optimizer that recouples the beneficial components identified through their decoupling analysis. This optimizer achieves better optimization efficiency and representation effectiveness simultaneously by enabling more controllable regularization of primitive attributes.

Contribution

Autonomous redundancy removal without additional pruning operations

The authors demonstrate that their AdamW-GS method can automatically remove redundant primitives in vanilla 3DGS through proper regularization alone, without requiring extra pruning components. This is achieved while maintaining or improving reconstruction quality and optimization efficiency.

Table of Contents