Refining Gaussian Splatting: A Volumetric Densification Approach

• URL: http://arxiv.org/abs/2508.05187v1
• Date: Thu, 07 Aug 2025 09:23:17 GMT
• Title: Refining Gaussian Splatting: A Volumetric Densification Approach
• Abstract summary: We introduce a novel density control method, which exploits the volumes of inertia associated to each Gaussian function to guide the refinement process.<n>Our approach surpasses 3DGS in reconstruction quality, delivering encouraging performance across diverse scenes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Achieving high-quality novel view synthesis in 3D Gaussian Splatting (3DGS) often depends on effective point primitive management. The underlying Adaptive Density Control (ADC) process addresses this issue by automating densification and pruning. Yet, the vanilla 3DGS densification strategy shows key shortcomings. To address this issue, in this paper we introduce a novel density control method, which exploits the volumes of inertia associated to each Gaussian function to guide the refinement process. Furthermore, we study the effect of both traditional Structure from Motion (SfM) and Deep Image Matching (DIM) methods for point cloud initialization. Extensive experimental evaluations on the Mip-NeRF 360 dataset demonstrate that our approach surpasses 3DGS in reconstruction quality, delivering encouraging performance across diverse scenes.

Related papers

• Beyond Heuristics: Learnable Density Control for 3D Gaussian Splatting [78.96382179408054]
  We introduce bfLeGS, a framework that reformulates density control as a parameterized policy network optimized via Reinforcement Learning (RL)<n>To maintain computational tractability, we derive a closed-form solution that reduces the complexity of reward calculation from $O(N2)$ to $O(N)$.<n>Experiments on the Mip-NeRF 360, Tanks & Temples, and Deep Blending datasets demonstrate that bfLeGS significantly outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-05-01T05:04:49Z)
• RobustSplat++: Decoupling Densification, Dynamics, and Illumination for In-the-Wild 3DGS [85.90134051583368]
  3D Gaussian Splatting (3DGS) 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 in-the-wild scenes affected by transient objects and illuminations.<n>We propose RobustSplat++, a robust solution based on several critical designs.
  arXiv  Detail & Related papers  (2025-12-04T14:05:09Z)
• DensifyBeforehand: LiDAR-assisted Content-aware Densification for Efficient and Quality 3D Gaussian Splatting [1.5576275034099496]
  This paper addresses the limitations of existing 3D Gaussian Splatting (3DGS) methods by combining sparse LiDAR data with monocular depth estimation from corresponding RGB images.<n>Our ROI-aware sampling scheme prioritizes semantically and geometrically important regions, yielding a dense point cloud.<n>Our method achieves comparable results to state-of-the-art techniques while significantly lowering resource consumption and training time.
  arXiv  Detail & Related papers  (2025-11-24T16:39:13Z)
• 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)
• Intern-GS: Vision Model Guided Sparse-View 3D Gaussian Splatting [95.61137026932062]
  Intern-GS is a novel approach to enhance the process of sparse-view Gaussian splatting.<n>We show that Intern-GS achieves state-of-the-art rendering quality across diverse datasets.
  arXiv  Detail & Related papers  (2025-05-27T05:17:49Z)
• Steepest Descent Density Control for Compact 3D Gaussian Splatting [72.54055499344052]
  3D Gaussian Splatting (3DGS) has emerged as a powerful real-time, high-resolution novel view.<n>We propose a theoretical framework that demystifies and improves density control in 3DGS.<n>We introduce SteepGS, incorporating steepest density control, a principled strategy that minimizes loss while maintaining a compact point cloud.
  arXiv  Detail & Related papers  (2025-05-08T18:41:38Z)
• GP-GS: Gaussian Processes for Enhanced Gaussian Splatting [15.263608848427136]
  This paper proposes a novel 3D reconstruction framework, Gaussian Processes enhanced Gaussian Splatting (GP-GS)<n>GP-GS enables adaptive and uncertainty-guided densification of sparse Structure-from-Motion point clouds.<n>Experiments conducted on synthetic and real-world datasets validate the effectiveness and practicality of the proposed framework.
  arXiv  Detail & Related papers  (2025-02-04T12:50:16Z)
• ResGS: Residual Densification of 3D Gaussian for Efficient Detail Recovery [11.706262924395768]
  We introduce a novel densification operation, residual split, which adds a downscaled Gaussian as a residual.<n>Our approach is capable of adaptively retrieving details and complementing missing geometry.
  arXiv  Detail & Related papers  (2024-12-10T13:19:27Z)
• MVG-Splatting: Multi-View Guided Gaussian Splatting with Adaptive Quantile-Based Geometric Consistency Densification [8.099621725105857]
  We introduce MVG-Splatting, a solution guided by Multi-View considerations. We propose an adaptive quantile-based method that dynamically determines the level of additional densification. This approach significantly enhances the overall fidelity and accuracy of the 3D reconstruction process.
  arXiv  Detail & Related papers  (2024-07-16T15:24:01Z)
• 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)
• Revising Densification in Gaussian Splatting [23.037676471903215]
  We introduce a pixel-error driven formulation for density control in 3DGS, leveraging an auxiliary, per-pixel error function as the criterion for densification. Our approach leads to consistent quality improvements across a variety of benchmark scenes, without sacrificing the method's efficiency.
  arXiv  Detail & Related papers  (2024-04-09T08:20:37Z)
• Motion-aware 3D Gaussian Splatting for Efficient Dynamic Scene Reconstruction [89.53963284958037]
  We propose a novel motion-aware enhancement framework for dynamic scene reconstruction. Specifically, we first establish a correspondence between 3D Gaussian movements and pixel-level flow. For the prevalent deformation-based paradigm that presents a harder optimization problem, a transient-aware deformation auxiliary module is proposed.
  arXiv  Detail & Related papers  (2024-03-18T03:46:26Z)
• 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.