Temporally Compressed 3D Gaussian Splatting for Dynamic Scenes

• URL: http://arxiv.org/abs/2412.05700v1
• Date: Sat, 07 Dec 2024 17:03:09 GMT
• Title: Temporally Compressed 3D Gaussian Splatting for Dynamic Scenes
• Authors: Saqib Javed, Ahmad Jarrar Khan, Corentin Dumery, Chen Zhao, Mathieu Salzmann,
• Abstract summary: Temporally Compressed 3D Gaussian Splatting (TC3DGS) is a novel technique designed specifically to compress dynamic 3D Gaussian representations. Our experiments across multiple datasets demonstrate that TC3DGS achieves up to 67$times$ compression with minimal or no degradation in visual quality.
• Score: 46.64784407920817
• License:
• Abstract: Recent advancements in high-fidelity dynamic scene reconstruction have leveraged dynamic 3D Gaussians and 4D Gaussian Splatting for realistic scene representation. However, to make these methods viable for real-time applications such as AR/VR, gaming, and rendering on low-power devices, substantial reductions in memory usage and improvements in rendering efficiency are required. While many state-of-the-art methods prioritize lightweight implementations, they struggle in handling scenes with complex motions or long sequences. In this work, we introduce Temporally Compressed 3D Gaussian Splatting (TC3DGS), a novel technique designed specifically to effectively compress dynamic 3D Gaussian representations. TC3DGS selectively prunes Gaussians based on their temporal relevance and employs gradient-aware mixed-precision quantization to dynamically compress Gaussian parameters. It additionally relies on a variation of the Ramer-Douglas-Peucker algorithm in a post-processing step to further reduce storage by interpolating Gaussian trajectories across frames. Our experiments across multiple datasets demonstrate that TC3DGS achieves up to 67$\times$ compression with minimal or no degradation in visual quality.

Related papers

• Speedy-Splat: Fast 3D Gaussian Splatting with Sparse Pixels and Sparse Primitives [60.217580865237835]
  3D Gaussian Splatting (3D-GS) is a recent 3D scene reconstruction technique that enables real-time rendering of novel views by modeling scenes as parametric point clouds of differentiable 3D Gaussians. We identify and address two key inefficiencies in 3D-GS, achieving substantial improvements in rendering speed, model size, and training time. Our Speedy-Splat approach combines these techniques to accelerate average rendering speed by a drastic $6.71times$ across scenes from the Mip-NeRF 360, Tanks & Temples, and Deep Blending datasets with $10.6times$ fewer primitives than 3
  arXiv  Detail & Related papers  (2024-11-30T20:25:56Z)
• 3D Convex Splatting: Radiance Field Rendering with 3D Smooth Convexes [87.01284850604495]
  We introduce 3D Convexting (3DCS), which leverages 3D smooth convexes as primitives for modeling geometrically-meaningful radiance fields from multiview images. 3DCS achieves superior performance over 3DGS on benchmarks such as MipNeizer, Tanks and Temples, and Deep Blending. Our results highlight the potential of 3D Convexting to become the new standard for high-quality scene reconstruction.
  arXiv  Detail & Related papers  (2024-11-22T14:31:39Z)
• Fully Explicit Dynamic Gaussian Splatting [22.889981393105554]
  3D Gaussian Splatting has shown fast and high-quality rendering results in static scenes by leveraging dense 3D prior and explicit representations. We introduce a progressive training scheme and a point-backtracking technique that improves Ex4DGS's convergence. Comprehensive experiments on various scenes demonstrate the state-of-the-art rendering quality from our method, achieving fast rendering of 62 fps on a single 2080Ti GPU.
  arXiv  Detail & Related papers  (2024-10-21T04:25:43Z)
• MEGA: Memory-Efficient 4D Gaussian Splatting for Dynamic Scenes [49.36091070642661]
  This paper introduces a memory-efficient framework for 4DGS. It achieves a storage reduction by approximately 190$times$ and 125$times$ on the Technicolor and Neural 3D Video datasets. It maintains comparable rendering speeds and scene representation quality, setting a new standard in the field.
  arXiv  Detail & Related papers  (2024-10-17T14:47:08Z)
• 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)
• PUP 3D-GS: Principled Uncertainty Pruning for 3D Gaussian Splatting [59.277480452459315]
  We propose a principled sensitivity pruning score that preserves visual fidelity and foreground details at significantly higher compression ratios. We also propose a multi-round prune-refine pipeline that can be applied to any pretrained 3D-GS model without changing its training pipeline.
  arXiv  Detail & Related papers  (2024-06-14T17:53:55Z)
• A Refined 3D Gaussian Representation for High-Quality Dynamic Scene Reconstruction [2.022451212187598]
  In recent years, Neural Radiance Fields (NeRF) has revolutionized three-dimensional (3D) reconstruction with its implicit representation. 3D Gaussian Splatting (3D-GS) has departed from the implicit representation of neural networks and instead directly represents scenes as point clouds with Gaussian-shaped distributions. This paper purposes a refined 3D Gaussian representation for high-quality dynamic scene reconstruction. Experimental results demonstrate that our method surpasses existing approaches in rendering quality and speed, while significantly reducing the memory usage associated with 3D-GS.
  arXiv  Detail & Related papers  (2024-05-28T07:12:22Z)
• SAGS: Structure-Aware 3D Gaussian Splatting [53.6730827668389]
  We propose a structure-aware Gaussian Splatting method (SAGS) that implicitly encodes the geometry of the scene. SAGS reflects to state-of-the-art rendering performance and reduced storage requirements on benchmark novel-view synthesis datasets.
  arXiv  Detail & Related papers  (2024-04-29T23:26:30Z)
• Compact 3D Gaussian Representation for Radiance Field [14.729871192785696]
  We propose a learnable mask strategy to reduce the number of 3D Gaussian points without sacrificing performance. We also 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  (2023-11-22T20:31:16Z)
• Compressed 3D Gaussian Splatting for Accelerated Novel View Synthesis [0.552480439325792]
  High-fidelity scene reconstruction with an optimized 3D Gaussian splat representation has been introduced for novel view synthesis from sparse image sets. We propose a compressed 3D Gaussian splat representation that utilizes sensitivity-aware vector clustering with quantization-aware training to compress directional colors and Gaussian parameters.
  arXiv  Detail & Related papers  (2023-11-17T14:40:43Z)

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.