Faster-GS: Analyzing and Improving Gaussian Splatting Optimization

• URL: http://arxiv.org/abs/2602.09999v1
• Date: Tue, 10 Feb 2026 17:22:59 GMT
• Title: Faster-GS: Analyzing and Improving Gaussian Splatting Optimization
• Authors: Florian Hahlbohm, Linus Franke, Martin Eisemann, Marcus Magnor,
• Abstract summary: We consolidate and evaluate the most effective and broadly applicable strategies from prior 3DGS research.<n> Faster-GS provides a rigorously optimized algorithm that we evaluate across a comprehensive suite of benchmarks.<n>Our experiments demonstrate that Faster-GS achieves up to 5$times$ faster training while maintaining visual quality.
• Score: 1.2949520455740091
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in 3D Gaussian Splatting (3DGS) have focused on accelerating optimization while preserving reconstruction quality. However, many proposed methods entangle implementation-level improvements with fundamental algorithmic modifications or trade performance for fidelity, leading to a fragmented research landscape that complicates fair comparison. In this work, we consolidate and evaluate the most effective and broadly applicable strategies from prior 3DGS research and augment them with several novel optimizations. We further investigate underexplored aspects of the framework, including numerical stability, Gaussian truncation, and gradient approximation. The resulting system, Faster-GS, provides a rigorously optimized algorithm that we evaluate across a comprehensive suite of benchmarks. Our experiments demonstrate that Faster-GS achieves up to 5$\times$ faster training while maintaining visual quality, establishing a new cost-effective and resource efficient baseline for 3DGS optimization. Furthermore, we demonstrate that optimizations can be applied to 4D Gaussian reconstruction, leading to efficient non-rigid scene optimization.

Related papers

• A Step to Decouple Optimization in 3DGS [38.797134528503015]
  3D Gaussian Splatting (3DGS) has emerged as a powerful technique for real-time novel view synthesis.<n>In this paper, we take a step to decouple the process into: Sparse Adam, Re-State Regularization and Decoupled Attribute Regularization.
  arXiv  Detail & Related papers  (2026-01-23T13:34:39Z)
• Opt3DGS: Optimizing 3D Gaussian Splatting with Adaptive Exploration and Curvature-Aware Exploitation [10.150288678666001]
  3D Splatting (3DGS) has emerged as a leading framework for novel view synthesis, yet its core optimization challenges remain underexplored.<n>We identify two key issues in 3DGS optimization: entrapment in suboptimal local optima and insufficient convergence quality.<n>We propose Opt3DGS, a robust framework that enhances 3DGS through a two-stage optimization process.
  arXiv  Detail & Related papers  (2025-11-17T16:37:33Z)
• QuickSplat: Fast 3D Surface Reconstruction via Learned Gaussian Initialization [69.50126552763157]
  Surface reconstruction is fundamental to computer vision and graphics, enabling applications in 3D modeling, mixed reality, robotics, and more.<n>Existing approaches based on rendering obtain promising results, but optimize on a per-scene basis, resulting in a slow optimization that can struggle to model textureless regions.<n>We introduce QuickSplat, which learns data-driven priors to generate dense initializations for 2D gaussian splatting optimization of large-scale indoor scenes.
  arXiv  Detail & Related papers  (2025-05-08T18:43:26Z)
• FreeSplat++: Generalizable 3D Gaussian Splatting for Efficient Indoor Scene Reconstruction [50.534213038479926]
  FreeSplat++ is an alternative approach to large-scale indoor whole-scene reconstruction.<n>Our method with depth-regularized per-scene fine-tuning demonstrates substantial improvements in reconstruction accuracy and a notable reduction in training time.
  arXiv  Detail & Related papers  (2025-03-29T06:22:08Z)
• DashGaussian: Optimizing 3D Gaussian Splatting in 200 Seconds [71.37326848614133]
  We propose DashGaussian, a scheduling scheme over the optimization complexity of 3DGS.<n>We show that our method accelerates the optimization of various 3DGS backbones by 45.7% on average.
  arXiv  Detail & Related papers  (2025-03-24T07:17:27Z)
• Mini-Splatting2: Building 360 Scenes within Minutes via Aggressive Gaussian Densification [4.733612131945549]
  Mini-Splatting2 achieves a balanced trade-off among optimization time, the number of Gaussians, and rendering quality. Our work sets the stage for more efficient, high-quality 3D scene modeling in real-world applications.
  arXiv  Detail & Related papers  (2024-11-19T11:47:40Z)
• 3DGS-LM: Faster Gaussian-Splatting Optimization with Levenberg-Marquardt [65.25603275491544]
  We present 3DGS-LM, a new method that accelerates the reconstruction of 3D Gaussian Splatting (3DGS)<n>Our method is 20% faster than the original 3DGS while obtaining the same reconstruction quality optimization.
  arXiv  Detail & Related papers  (2024-09-19T16:31:44Z)
• An Empirical Evaluation of Zeroth-Order Optimization Methods on AI-driven Molecule Optimization [78.36413169647408]
  We study the effectiveness of various ZO optimization methods for optimizing molecular objectives. We show the advantages of ZO sign-based gradient descent (ZO-signGD) We demonstrate the potential effectiveness of ZO optimization methods on widely used benchmark tasks from the Guacamol suite.
  arXiv  Detail & Related papers  (2022-10-27T01:58:10Z)
• A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning [95.85269649177336]
  ZO optimization iteratively performs three major steps: gradient estimation, descent direction, and solution update. We demonstrate promising applications of ZO optimization, such as evaluating and generating explanations from black-box deep learning models, and efficient online sensor management.
  arXiv  Detail & Related papers  (2020-06-11T06:50:35Z)

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.