Flow4DGS-SLAM: Optical Flow-Guided 4D Gaussian Splatting SLAM

• URL: http://arxiv.org/abs/2604.22339v2
• Date: Tue, 28 Apr 2026 08:36:34 GMT
• Title: Flow4DGS-SLAM: Optical Flow-Guided 4D Gaussian Splatting SLAM
• Abstract summary: We propose an efficient framework for dynamic 3DGS SLAM guided by optical flow.<n>We boost the training speed of dynamic 3DGS by explicitly modeling their temporal centers.<n>The empirical results demonstrate our state-of-the-art performance in tracking, dynamic reconstruction, and training efficiency.
• Score: 55.56111618153049
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Handling the dynamic environments is a significant research challenge in Visual Simultaneous Localization and Mapping (SLAM). Recent research combines 3D Gaussian Splatting (3DGS) with SLAM to achieve both robust camera pose estimation and photorealistic renderings. However, using SLAM to efficiently reconstruct both static and dynamic regions remains challenging. In this work, we propose an efficient framework for dynamic 3DGS SLAM guided by optical flow. Using the input depth and prior optical flow, we first propose a category-agnostic motion mask generation strategy by fitting a camera ego-motion model to decompose the optical flow. This module separates dynamic and static Gaussians and simultaneously provides flow-guided camera pose initialization. We boost the training speed of dynamic 3DGS by explicitly modeling their temporal centers at keyframes. These centers are propagated using 3D scene flow priors and are dynamically initialized with an adaptive insertion strategy. Alongside this, we model the temporal opacity and rotation using a Gaussian Mixture Model (GMM) to adaptively learn the complex dynamics. The empirical results demonstrate our state-of-the-art performance in tracking, dynamic reconstruction, and training efficiency.

Related papers

• GGD-SLAM: Monocular 3DGS SLAM Powered by Generalizable Motion Model for Dynamic Environments [7.864663125771112]
  GGD-SLAM is a framework that employs a generalizable motion model to address the challenges of localization and dense mapping in dynamic environments.<n>It is integrated with a dynamic feature enhancer to separate static and dynamic components.<n>Experiments conducted on real-world dynamic datasets demonstrate that the proposed system achieves state-of-the-art performance in camera pose estimation and dense reconstruction in dynamic scenes.
  arXiv  Detail & Related papers  (2026-04-14T14:58:11Z)
• FastPhysGS: Accelerating Physics-based Dynamic 3DGS Simulation via Interior Completion and Adaptive Optimization [56.17833729527066]
  We propose FastPhysGS, a framework for physics-based dynamic 3DGS simulation.<n>FastPhysGS achieves high-fidelity physical simulation in 1 minute using only 7 GB runtime memory.
  arXiv  Detail & Related papers  (2026-02-02T07:00:42Z)
• VIGS-SLAM: Visual Inertial Gaussian Splatting SLAM [75.55522219717137]
  We present VIGS-SLAM, a visual-inertial 3D Gaussian Splatting SLAM system.<n>It achieves robust real-time tracking and high-fidelity reconstruction.<n>Our method tightly couples visual and inertial cues within a unified optimization framework.
  arXiv  Detail & Related papers  (2025-12-02T00:19:13Z)
• 4D3R: Motion-Aware Neural Reconstruction and Rendering of Dynamic Scenes from Monocular Videos [52.89084603734664]
  We present 4D3R, a pose-free dynamic neural rendering framework that decouples static and dynamic components through a two-stage approach.<n>Our approach achieves up to 1.8dB PSNR improvement over state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-11-07T13:25:50Z)
• Laplacian Analysis Meets Dynamics Modelling: Gaussian Splatting for 4D Reconstruction [9.911802466255653]
  We propose a novel dynamic 3DGS framework with hybrid explicit-implicit functions.<n>Our method demonstrates state-of-the-art performance in reconstructing complex dynamic scenes, achieving better reconstruction fidelity.
  arXiv  Detail & Related papers  (2025-08-07T01:39:29Z)
• Embracing Dynamics: Dynamics-aware 4D Gaussian Splatting SLAM [0.0]
  D4DGS-SLAM is the first SLAM method based on 4DGS map representation for dynamic environments.<n>We show that our method outperforms state-of-the-art approaches in both camera pose tracking and map quality.
  arXiv  Detail & Related papers  (2025-04-07T08:56:35Z)
• Event-boosted Deformable 3D Gaussians for Dynamic Scene Reconstruction [50.873820265165975]
  We introduce the first approach combining event cameras, which capture high-temporal-resolution, continuous motion data, with deformable 3D-GS for dynamic scene reconstruction.<n>We propose a GS-Threshold Joint Modeling strategy, creating a mutually reinforcing process that greatly improves both 3D reconstruction and threshold modeling.<n>We contribute the first event-inclusive 4D benchmark with synthetic and real-world dynamic scenes, on which our method achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-11-25T08:23:38Z)
• Gaussian Splatting Lucas-Kanade [0.11249583407496218]
  We propose a novel analytical approach that adapts the classical Lucas-Kanade method to dynamic Gaussian splatting.<n>By leveraging the intrinsic properties of the forward warp field network, we derive an analytical velocity field that, through time integration, facilitates accurate scene flow computation.<n>Our method excels in reconstructing highly dynamic scenes with minimal camera movement, as demonstrated through experiments on both synthetic and real-world scenes.
  arXiv  Detail & Related papers  (2024-07-16T01:50:43Z)
• 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)

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.