GS-NFS: Bandwidth-adaptive Streaming of Dynamic Gaussian Splats and Point Clouds

• URL: http://arxiv.org/abs/2606.05650v1
• Date: Thu, 04 Jun 2026 03:27:56 GMT
• Title: GS-NFS: Bandwidth-adaptive Streaming of Dynamic Gaussian Splats and Point Clouds
• Abstract summary: GS-NFS accelerates dynamic 3DGS compression and decompression on a GPU.<n>It is 1-2 orders of magnitude faster than the state-of-the-art in encoding and decoding a frame.
• Score: 27.401745355020086
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dynamic 3D Gaussian Splatting (3DGS) holds great promise as a 3D video streaming technology since it can represent complex 3D scenes with high fidelity. In this approach, every frame in a 3D video represents the environment as a collection of Gaussians with position and other attributes such as scale, rotation, opacity, and color. Frames capture fine details, permit views from any arbitrary perspective, but are an order of magnitude, or more, larger than 2D video frames. A line of recent work has explored how to compress dynamic 3DGS frames, but these approaches are often slow, in part because their compression techniques are not amenable to efficient acceleration. GS-NFS accelerates dynamic 3DGS compression and decompression on a GPU, to the point where it can encode and decode at full frame rate. It achieves this by developing novel GPU-based parallelizations of existing algorithms for encoding both positions and attributes of Gaussians. As a result, it is 1-2 orders of magnitude faster than the state-of-the-art in encoding and decoding a frame, while offering competitive compression performance and rendering quality.

Related papers

• Compression of 3D Gaussian Splatting Data Using GPU-friendly Graphics Texture Coding [2.740558005015499]
  In 3DGS, spherical harmonic (SH) are employed to model view-dependent color, resulting in a large number of SH coefficients per primitive and large memory requirements.<n>We propose a method for compressing SH color coefficients using texture compression schemes specifically designed for efficient parallel GPU decoding.
  arXiv  Detail & Related papers  (2026-07-16T03:10:27Z)
• D-FCGS: Feedforward Compression of Dynamic Gaussian Splatting for Free-Viewpoint Videos [12.24209693552492]
  Free-viewpoint video (FVV) enables immersive 3D experiences, but efficient compression of dynamic 3D representations remains a major challenge.<n>This paper presents Feedforward Compression of Dynamic Gaussian Splatting (D-FCGS), a novel feedforward framework for compressing temporally correlated Gaussian point cloud sequences.<n> Experiments show that it matches the rate-distortion performance of optimization-based methods, achieving over 40 times compression in under 2 seconds.
  arXiv  Detail & Related papers  (2025-07-08T10:39:32Z)
• GSVR: 2D Gaussian-based Video Representation for 800+ FPS with Hybrid Deformation Field [7.977026024810772]
  Implicit neural representations for video have been recognized as a novel and promising video representation.<n>We propose GSVR, a novel 2D Gaussian-based video representation, which achieves 800+ FPS and 35+ PSNR on Bunny.<n>Our method converges much faster than existing methods and also has 10x faster decoding speed compared to other methods.
  arXiv  Detail & Related papers  (2025-07-08T02:13:12Z)
• Speedy Deformable 3D Gaussian Splatting: Fast Rendering and Compression of Dynamic Scenes [57.69608119350651]
  Recent extensions of 3D Gaussian Splatting (3DGS) to dynamic scenes achieve high-quality novel view synthesis by using neural networks to predict the time-varying deformation of each Gaussian.<n>However, performing per-Gaussian neural inference at every frame poses a significant bottleneck, limiting rendering speed and increasing memory and compute requirements.<n>We present Speedy Deformable 3D Gaussian Splatting (SpeeDe3DGS), a general pipeline for accelerating the rendering speed of dynamic 3DGS and 4DGS representations by reducing neural inference through two complementary techniques.
  arXiv  Detail & Related papers  (2025-06-09T16:30:48Z)
• Sparse VideoGen: Accelerating Video Diffusion Transformers with Spatial-Temporal Sparsity [59.80405282381126]
  Diffusion Transformers (DiTs) dominate video generation but their high computational cost severely limits real-world applicability.<n>We propose a training-free framework termed Sparse VideoGen (SVG) that leverages the inherent sparsity in 3D Full Attention to boost inference efficiency.<n>SVG achieves up to 2.28x and 2.33x end-to-end speedup on CogVideoX-v1.5 and HunyuanVideo, respectively, while preserving generation quality.
  arXiv  Detail & Related papers  (2025-02-03T19:29:16Z)
• GSVC: Efficient Video Representation and Compression Through 2D Gaussian Splatting [3.479384894190067]
  We propose GSVC, an approach to learning a set of 2D Gaussian splats that can effectively represent and compress video frames.<n>Experiment results show that GSVC achieves good rate-distortion trade-offs, comparable to state-of-the-art video codecs.
  arXiv  Detail & Related papers  (2025-01-21T11:30:51Z)
• Temporally Compressed 3D Gaussian Splatting for Dynamic Scenes [46.64784407920817]
  Temporally Compressed 3D Gaussian Splatting (TC3DGS) is a novel technique designed specifically to compress dynamic 3D Gaussian representations.<n>Our experiments across multiple datasets demonstrate that TC3DGS achieves up to 67$times$ compression with minimal or no degradation in visual quality.
  arXiv  Detail & Related papers  (2024-12-07T17:03:09Z)
• Fast Feedforward 3D Gaussian Splatting Compression [55.149325473447384]
  3D Gaussian Splatting (FCGS) is an optimization-free model that can compress 3DGS representations rapidly in a single feed-forward pass.<n>FCGS achieves a compression ratio of over 20X while maintaining fidelity, surpassing most per-scene SOTA optimization-based methods.
  arXiv  Detail & Related papers  (2024-10-10T15:13:08Z)
• Compact 3D Gaussian Splatting for Static and Dynamic Radiance Fields [13.729716867839509]
  We propose a learnable mask strategy that significantly reduces the number of Gaussians while preserving high performance. In addition, we propose a compact but effective representation of view-dependent color by employing a grid-based neural field. Our work provides a comprehensive framework for 3D scene representation, achieving high performance, fast training, compactness, and real-time rendering.
  arXiv  Detail & Related papers  (2024-08-07T14:56:34Z)
• Enhancing Temporal Consistency in Video Editing by Reconstructing Videos with 3D Gaussian Splatting [94.84688557937123]
  Video-3DGS is a 3D Gaussian Splatting (3DGS)-based video refiner designed to enhance temporal consistency in zero-shot video editors.<n>Our approach utilizes a two-stage 3D Gaussian optimizing process tailored for editing dynamic monocular videos.<n>It enhances video editing by ensuring temporal consistency across 58 dynamic monocular videos.
  arXiv  Detail & Related papers  (2024-06-04T17:57:37Z)
• 4D Gaussian Splatting for Real-Time Dynamic Scene Rendering [103.32717396287751]
  We propose 4D Gaussian Splatting (4D-GS) as a holistic representation for dynamic scenes. A neuralvoxel encoding algorithm inspired by HexPlane is proposed to efficiently build features from 4D neural voxels. Our 4D-GS method achieves real-time rendering under high resolutions, 82 FPS at an 800$times$800 resolution on an 3090 GPU.
  arXiv  Detail & Related papers  (2023-10-12T17:21:41Z)

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.