Fast and Robust Deformable 3D Gaussian Splatting
- URL: http://arxiv.org/abs/2603.20857v1
- Date: Sat, 21 Mar 2026 15:24:54 GMT
- Title: Fast and Robust Deformable 3D Gaussian Splatting
- Abstract summary: 3D Gaussian Splatting has demonstrated remarkable real-time rendering capabilities and superior visual quality in novel view synthesis for static scenes.<n>Deformation field-based methods have emerged as a promising approach among various techniques.<n>We present FRoG, an efficient and robust framework for high-quality dynamic scene reconstruction.
- Score: 16.61618332955902
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 3D Gaussian Splatting has demonstrated remarkable real-time rendering capabilities and superior visual quality in novel view synthesis for static scenes. Building upon these advantages, researchers have progressively extended 3D Gaussians to dynamic scene reconstruction. Deformation field-based methods have emerged as a promising approach among various techniques. These methods maintain 3D Gaussian attributes in a canonical field and employ the deformation field to transform this field across temporal sequences. Nevertheless, these approaches frequently encounter challenges such as suboptimal rendering speeds, significant dependence on initial point clouds, and vulnerability to local optima in dim scenes. To overcome these limitations, we present FRoG, an efficient and robust framework for high-quality dynamic scene reconstruction. FRoG integrates per-Gaussian embedding with a coarse-to-fine temporal embedding strategy, accelerating rendering through the early fusion of temporal embeddings. Moreover, to enhance robustness against sparse initializations, we introduce a novel depth- and error-guided sampling strategy. This strategy populates the canonical field with new 3D Gaussians at low-deviation initial positions, significantly reducing the optimization burden on the deformation field and improving detail reconstruction in both static and dynamic regions. Furthermore, by modulating opacity variations, we mitigate the local optima problem in dim scenes, improving color fidelity. Comprehensive experimental results validate that our method achieves accelerated rendering speeds while maintaining state-of-the-art visual quality.
Related papers
- Improving Sparse-View 3DGS Generalization via Flat Minima Optimization [37.65568887478836]
Recent advances in neural rendering have established 3D Gaussian Splatting (3DGS) as a highly efficient representation for novel view synthesis.<n>However, when supervision is limited to sparse input views, 3DGS tends to overfit to the observed images and generalize poorly to unseen viewpoints.<n>We address this challenge from the perspective of flat minima (FM) optimization, which seeks solutions that remain stable under small parameter perturbations.
arXiv Detail & Related papers (2026-07-01T12:52:57Z) - TG-Field: Geometry-Aware Radiative Gaussian Fields for Tomographic Reconstruction [16.246538335191982]
Tomographic Geometry Field (TG-Field) is a geometry-aware Gaussian deformation framework for computed tomography (CT) reconstruction.<n> TG-Field consistently outperforms existing methods, achieving state-of-the-art reconstruction accuracy under highly sparse-view conditions.
arXiv Detail & Related papers (2026-02-12T08:33:01Z) - 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) - MAPo : Motion-Aware Partitioning of Deformable 3D Gaussian Splatting for High-Fidelity Dynamic Scene Reconstruction [13.28997490352807]
We introduce Motion-Aware Partitioning of Deformable 3D Gaussian Splatting (MAPo)<n>MAPo is a novel framework for high-fidelity dynamic scene reconstruction.<n>It achieves superior rendering quality compared to baselines while maintaining comparable computational costs.
arXiv Detail & Related papers (2025-08-27T11:10:46Z) - VDEGaussian: Video Diffusion Enhanced 4D Gaussian Splatting for Dynamic Urban Scenes Modeling [68.65587507038539]
We present a novel video diffusion-enhanced 4D Gaussian Splatting framework for dynamic urban scene modeling.<n>Our key insight is to distill robust, temporally consistent priors from a test-time adapted video diffusion model.<n>Our method significantly enhances dynamic modeling, especially for fast-moving objects, achieving an approximate PSNR gain of 2 dB.
arXiv Detail & Related papers (2025-08-04T07:24:05Z) - FreeTimeGS: Free Gaussian Primitives at Anytime and Anywhere for Dynamic Scene Reconstruction [64.30050475414947]
FreeTimeGS is a novel 4D representation that allows Gaussian primitives to appear at arbitrary time and locations.<n>Our representation possesses the strong flexibility, thus improving the ability to model dynamic 3D scenes.<n> Experiments results on several datasets show that the rendering quality of our method outperforms recent methods by a large margin.
arXiv Detail & Related papers (2025-06-05T17:59:57Z) - 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) - EvolvingGS: High-Fidelity Streamable Volumetric Video via Evolving 3D Gaussian Representation [14.402479944396665]
We introduce EvolvingGS, a two-stage strategy that first deforms the Gaussian model to align with the target frame, and then refines it with minimal point addition/subtraction.<n> Owing to the flexibility of the incrementally evolving representation, our method outperforms existing approaches in terms of both per-frame and temporal quality metrics.<n>Our method significantly advances the state-of-the-art in dynamic scene reconstruction, particularly for extended sequences with complex human performances.
arXiv Detail & Related papers (2025-03-07T06:01:07Z) - MCGS: Multiview Consistency Enhancement for Sparse-View 3D Gaussian Radiance Fields [100.90743697473232]
Radiance fields represented by 3D Gaussians excel at synthesizing novel views, offering both high training efficiency and fast rendering.<n>Existing methods often incorporate depth priors from dense estimation networks but overlook the inherent multi-view consistency in input images.<n>We propose a view synthesis framework based on 3D Gaussian Splatting, enabling scene reconstruction from sparse views.
arXiv Detail & Related papers (2024-10-15T08:39:05Z) - GaussianRoom: Improving 3D Gaussian Splatting with SDF Guidance and Monocular Cues for Indoor Scene Reconstruction [5.112375652774415]
We propose a unified optimization framework that integrates neural signed distance fields (SDFs) with 3DGS for accurate geometry reconstruction and real-time rendering.<n>Our method achieves state-of-the-art performance in both surface reconstruction and novel view synthesis.
arXiv Detail & Related papers (2024-05-30T03:46:59Z) - VastGaussian: Vast 3D Gaussians for Large Scene Reconstruction [59.40711222096875]
We present VastGaussian, the first method for high-quality reconstruction and real-time rendering on large scenes based on 3D Gaussian Splatting.
Our approach outperforms existing NeRF-based methods and achieves state-of-the-art results on multiple large scene datasets.
arXiv Detail & Related papers (2024-02-27T11:40:50Z) - Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering [71.44349029439944]
Recent 3D Gaussian Splatting method has achieved the state-of-the-art rendering quality and speed.
We introduce Scaffold-GS, which uses anchor points to distribute local 3D Gaussians.
We show that our method effectively reduces redundant Gaussians while delivering high-quality rendering.
arXiv Detail & Related papers (2023-11-30T17:58:57Z) - Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene
Reconstruction [29.83056271799794]
Implicit neural representation has paved the way for new approaches to dynamic scene reconstruction and rendering.
We propose a deformable 3D Gaussians Splatting method that reconstructs scenes using 3D Gaussians and learns them in canonical space.
Through a differential Gaussianizer, the deformable 3D Gaussians not only achieve higher rendering quality but also real-time rendering speed.
arXiv Detail & Related papers (2023-09-22T16:04:02Z)
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.