Speedy-Splat: Fast 3D Gaussian Splatting with Sparse Pixels and Sparse Primitives

• URL: http://arxiv.org/abs/2412.00578v2
• Date: Mon, 24 Mar 2025 20:30:29 GMT
• Title: Speedy-Splat: Fast 3D Gaussian Splatting with Sparse Pixels and Sparse Primitives
• Authors: Alex Hanson, Allen Tu, Geng Lin, Vasu Singla, Matthias Zwicker, Tom Goldstein,
• Abstract summary: 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.<n>We identify and address two key inefficiencies in 3D-GS to substantially improve rendering speed.<n>Our Speedy-Splat approach combines these techniques to accelerate average rendering speed by a drastic $mathit6.71times$ across scenes from the Mip-NeRF 360, Tanks & Temples, and Deep Blending datasets.
• Score: 60.217580865237835
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. However, its rendering speed and model size still present bottlenecks, especially in resource-constrained settings. In this paper, we identify and address two key inefficiencies in 3D-GS to substantially improve rendering speed. These improvements also yield the ancillary benefits of reduced model size and training time. First, we optimize the rendering pipeline to precisely localize Gaussians in the scene, boosting rendering speed without altering visual fidelity. Second, we introduce a novel pruning technique and integrate it into the training pipeline, significantly reducing model size and training time while further raising rendering speed. Our Speedy-Splat approach combines these techniques to accelerate average rendering speed by a drastic $\mathit{6.71\times}$ across scenes from the Mip-NeRF 360, Tanks & Temples, and Deep Blending datasets.

Related papers

• EVolSplat: Efficient Volume-based Gaussian Splatting for Urban View Synthesis [61.1662426227688]
  Existing NeRF and 3DGS-based methods show promising results in achieving photorealistic renderings but require slow, per-scene optimization. We introduce EVolSplat, an efficient 3D Gaussian Splatting model for urban scenes that works in a feed-forward manner.
  arXiv  Detail & Related papers  (2025-03-26T02:47:27Z)
• 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)
• PUP 3D-GS: Principled Uncertainty Pruning for 3D Gaussian Splatting [59.277480452459315]
  We propose a principled spatial sensitivity pruning score that outperforms current approaches. We also propose a multi-round prune-refine pipeline that can be applied to any pretrained 3D-GS model. Our pipeline increases the average rendering speed of 3D-GS by 2.65$times$ while retaining more salient foreground information.
  arXiv  Detail & Related papers  (2024-06-14T17:53:55Z)
• 3D-HGS: 3D Half-Gaussian Splatting [5.766096863155448]
  Photo-realistic image rendering from scene 3D reconstruction is a fundamental problem in 3D computer vision. We introduce 3D Half-Gaussian kernels, which can be used as a plug-and-play kernel.
  arXiv  Detail & Related papers  (2024-06-04T19:04:29Z)
• Recent Advances in 3D Gaussian Splatting [31.3820273122585]
  3D Gaussian Splatting has greatly accelerated rendering speed of novel view synthesis. The explicit representation of 3D Gaussian Splatting facilitates editing tasks like dynamic reconstruction, geometry editing, and physical simulation. We present a literature review of recent 3D Gaussian Splatting methods, which can be roughly classified into 3D reconstruction, 3D editing, and other downstream applications.
  arXiv  Detail & Related papers  (2024-03-17T07:57:08Z)
• 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)
• EAGLES: Efficient Accelerated 3D Gaussians with Lightweight EncodingS [40.94643885302646]
  3D Gaussian splatting (3D-GS) has gained popularity in novel-view scene synthesis. It addresses the challenges of lengthy training times and slow rendering speeds associated with Radiance Neural Fields (NeRFs) We present a technique utilizing quantized embeddings to significantly reduce per-point memory storage requirements.
  arXiv  Detail & Related papers  (2023-12-07T18:59:55Z)
• 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)

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.