SDD-4DGS: Static-Dynamic Aware Decoupling in Gaussian Splatting for 4D Scene Reconstruction

• URL: http://arxiv.org/abs/2503.09332v1
• Date: Wed, 12 Mar 2025 12:25:58 GMT
• Title: SDD-4DGS: Static-Dynamic Aware Decoupling in Gaussian Splatting for 4D Scene Reconstruction
• Authors: Dai Sun, Huhao Guan, Kun Zhang, Xike Xie, S. Kevin Zhou,
• Abstract summary: This work introduces SDD-4DGS, the first framework for static-dynamic decoupled 4D scene reconstruction based on Gaussian Splatting.<n>Our approach is built upon a novel probabilistic dynamic perception coefficient that is naturally integrated into the Gaussian reconstruction pipeline.<n>Experiments on five benchmark datasets demonstrate that SDD-4DGS consistently outperforms state-of-the-art methods in reconstruction fidelity.
• Score: 21.822062121612166
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dynamic and static components in scenes often exhibit distinct properties, yet most 4D reconstruction methods treat them indiscriminately, leading to suboptimal performance in both cases. This work introduces SDD-4DGS, the first framework for static-dynamic decoupled 4D scene reconstruction based on Gaussian Splatting. Our approach is built upon a novel probabilistic dynamic perception coefficient that is naturally integrated into the Gaussian reconstruction pipeline, enabling adaptive separation of static and dynamic components. With carefully designed implementation strategies to realize this theoretical framework, our method effectively facilitates explicit learning of motion patterns for dynamic elements while maintaining geometric stability for static structures. Extensive experiments on five benchmark datasets demonstrate that SDD-4DGS consistently outperforms state-of-the-art methods in reconstruction fidelity, with enhanced detail restoration for static structures and precise modeling of dynamic motions. The code will be released.

Related papers

• UnIRe: Unsupervised Instance Decomposition for Dynamic Urban Scene Reconstruction [27.334884564978907]
  We propose UnIRe, a 3D Splatting (3DGS) based approach that decomposes a scene into a static background and individual dynamic instances. At its core, we introduce 4D superpoints, a novel representation that clusters multi-frame LiDAR points in 4D space. Experiments show that our method outperforms existing methods in dynamic scene reconstruction while enabling accurate and flexible instance-level editing.
  arXiv  Detail & Related papers  (2025-04-01T13:15:58Z)
• Divide-and-Conquer: Dual-Hierarchical Optimization for Semantic 4D Gaussian Spatting [16.15871890842964]
  We propose Dual-Hierarchical Optimization (DHO), which consists of Hierarchical Gaussian Flow and Hierarchical Gaussian Guidance. Our method consistently outperforms the baselines on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2025-03-25T03:46:13Z)
• CoDa-4DGS: Dynamic Gaussian Splatting with Context and Deformation Awareness for Autonomous Driving [12.006435326659526]
  We introduce a novel 4D Gaussian Splatting (4DGS) approach to improve dynamic scene rendering.<n> Specifically, we employ a 2D semantic segmentation foundation model to self-supervise the 4D semantic features of Gaussians.<n>By aggregating and encoding both semantic and temporal deformation features, each Gaussian is equipped with cues for potential deformation compensation.
  arXiv  Detail & Related papers  (2025-03-09T19:58:51Z)
• Not All Frame Features Are Equal: Video-to-4D Generation via Decoupling Dynamic-Static Features [14.03066701768256]
  We propose a dynamic-static feature decoupling module (DSFD) to enhance dynamic representations.<n>We acquire decoupled features driven by dynamic features and current frame features.<n>Along spatial axes, it adaptively selects similar information of dynamic regions.<n>Our method achieves state-of-the-art (SOTA) results in video-to-4D.
  arXiv  Detail & Related papers  (2025-02-12T13:08:35Z)
• 4D Gaussian Splatting: Modeling Dynamic Scenes with Native 4D Primitives [116.2042238179433]
  In this paper, we frame dynamic scenes as unconstrained 4D volume learning problems.<n>We represent a target dynamic scene using a collection of 4D Gaussian primitives with explicit geometry and appearance features.<n>This approach can capture relevant information in space and time by fitting the underlying photorealistic-temporal volume.<n> Notably, our 4DGS model is the first solution that supports real-time rendering of high-resolution, novel views for complex dynamic scenes.
  arXiv  Detail & Related papers  (2024-12-30T05:30:26Z)
• Urban4D: Semantic-Guided 4D Gaussian Splatting for Urban Scene Reconstruction [86.4386398262018]
  Urban4D is a semantic-guided decomposition strategy inspired by advances in deep 2D semantic map generation.<n>Our approach distinguishes potentially dynamic objects through reliable semantic Gaussians.<n>Experiments on real-world datasets demonstrate that Urban4D achieves comparable or better quality than previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-12-04T16:59:49Z)
• 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. We propose a GS-Threshold Joint Modeling strategy, creating a mutually reinforcing process that greatly improves both 3D reconstruction and threshold modeling. 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)
• Dynamics-Aware Gaussian Splatting Streaming Towards Fast On-the-Fly Training for 4D Reconstruction [12.111389926333592]
  Current 3DGS-based streaming methods treat the Gaussian primitives uniformly and constantly renew the densified Gaussians. We propose a novel three-stage pipeline for iterative streamable 4D dynamic spatial reconstruction. Our method achieves state-of-the-art performance in online 4D reconstruction, demonstrating a 20% improvement in on-the-fly training speed, superior representation quality, and real-time rendering capability.
  arXiv  Detail & Related papers  (2024-11-22T10:47:47Z)
• DeSiRe-GS: 4D Street Gaussians for Static-Dynamic Decomposition and Surface Reconstruction for Urban Driving Scenes [71.61083731844282]
  We present DeSiRe-GS, a self-supervised gaussian splatting representation. It enables effective static-dynamic decomposition and high-fidelity surface reconstruction in complex driving scenarios.
  arXiv  Detail & Related papers  (2024-11-18T05:49:16Z)
• SC4D: Sparse-Controlled Video-to-4D Generation and Motion Transfer [57.506654943449796]
  We propose an efficient, sparse-controlled video-to-4D framework named SC4D that decouples motion and appearance. Our method surpasses existing methods in both quality and efficiency. We devise a novel application that seamlessly transfers motion onto a diverse array of 4D entities.
  arXiv  Detail & Related papers  (2024-04-04T18:05:18Z)
• EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision [85.17951804790515]
  EmerNeRF is a simple yet powerful approach for learning spatial-temporal representations of dynamic driving scenes. It simultaneously captures scene geometry, appearance, motion, and semantics via self-bootstrapping. Our method achieves state-of-the-art performance in sensor simulation.
  arXiv  Detail & Related papers  (2023-11-03T17:59:55Z)

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.