3DGS$^2$-TR: Scalable Second-Order Trust-Region Method for 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2602.00395v1
• Date: Fri, 30 Jan 2026 23:14:09 GMT
• Title: 3DGS$^2$-TR: Scalable Second-Order Trust-Region Method for 3D Gaussian Splatting
• Authors: Roger Hsiao, Yuchen Fang, Xiangru Huang, Ruilong Li, Hesam Rabeti, Zan Gojcic, Javad Lavaei, James Demmel, Sophia Shao,
• Abstract summary: We propose 3DGS$2$-TR, a second-order computation for accelerating the training problem in 3D Gaussian Splatting (3DGS)<n>Our method approximates curvature using only the diagonal of the Hessian matrix, efficiently via Hutchinson's method.<n>3DGS$2$-TR is able to achieve better reconstruction quality on standard datasets, using 50% fewer training iterations compared to ADAM and 85% less than 3DGS-LM.
• Score: 25.277141070184083
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose 3DGS$^2$-TR,a second-order optimizer for accelerating the scene training problem in 3D Gaussian Splatting (3DGS). Unlike existing second-order approaches that rely on explicit or dense curvature representations, such as 3DGS-LM (Höllein et al., 2025) or 3DGS2 (Lan et al., 2025), our method approximates curvature using only the diagonal of the Hessian matrix, efficiently via Hutchinson's method. Our approach is fully matrix-free and has the same complexity as ADAM (Kingma, 2024), $O(n)$ in both computation and memory costs. To ensure stable optimization in the presence of strong nonlinearity in the 3DGS rasterization process, we introduce a parameter-wise trust-region technique based on the squared Hellinger distance, regularizing updates to Gaussian parameters. Under identical parameter initialization and without densification, 3DGS$^2$-TR is able to achieve better reconstruction quality on standard datasets, using 50% fewer training iterations compared to ADAM, while incurring less than 1GB of peak GPU memory overhead (17% more than ADAM and 85% less than 3DGS-LM), enabling scalability to very large scenes and potentially to distributed training settings.

Related papers

• CF3: Compact and Fast 3D Feature Fields [31.791638071734337]
  3D Gaussian Splatting (3DGS) has begun incorporating rich information from 2D foundation models.<n>We propose a top-down pipeline for constructing compact and fast 3D Gaussian feature fields, namely, CF3.
  arXiv  Detail & Related papers  (2025-08-07T10:45:08Z)
• Second-order Optimization of Gaussian Splats with Importance Sampling [51.95046424364725]
  3D Gaussian Splatting (3DGS) is widely used for novel view rendering due to its high quality and fast inference time.<n>We propose a novel second-order optimization strategy based on Levenberg-Marquardt (LM) and Conjugate Gradient (CG)<n>Our method achieves a $3times$ speedup over standard LM and outperforms Adam by $6times$ when the Gaussian count is low.
  arXiv  Detail & Related papers  (2025-04-17T12:52:08Z)
• 3DGS$^2$: Near Second-order Converging 3D Gaussian Splatting [26.94968605302451]
  3D Gaussian Splatting (3DGS) has emerged as a mainstream solution for novel view synthesis and 3D reconstruction.<n>This paper introduces a (near) second-order convergent training algorithm for 3DGS, leveraging its unique properties.
  arXiv  Detail & Related papers  (2025-01-22T22:28:11Z)
• 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)
• 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)
• LP-3DGS: Learning to Prune 3D Gaussian Splatting [71.97762528812187]
  We propose learning-to-prune 3DGS, where a trainable binary mask is applied to the importance score that can find optimal pruning ratio automatically. Experiments have shown that LP-3DGS consistently produces a good balance that is both efficient and high quality.
  arXiv  Detail & Related papers  (2024-05-29T05:58:34Z)
• 3DGSR: Implicit Surface Reconstruction with 3D Gaussian Splatting [58.95801720309658]
  In this paper, we present an implicit surface reconstruction method with 3D Gaussian Splatting (3DGS), namely 3DGSR.<n>The key insight is incorporating an implicit signed distance field (SDF) within 3D Gaussians to enable them to be aligned and jointly optimized.<n>Our experimental results demonstrate that our 3DGSR method enables high-quality 3D surface reconstruction while preserving the efficiency and rendering quality of 3DGS.
  arXiv  Detail & Related papers  (2024-03-30T16:35:38Z)
• 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)

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.