Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering

• URL: http://arxiv.org/abs/2312.00109v1
• Date: Thu, 30 Nov 2023 17:58:57 GMT
• Title: Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering
• Authors: Tao Lu, Mulin Yu, Linning Xu, Yuanbo Xiangli, Limin Wang, Dahua Lin, Bo Dai
• Abstract summary: 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.
• Score: 71.44349029439944
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural rendering methods have significantly advanced photo-realistic 3D scene rendering in various academic and industrial applications. The recent 3D Gaussian Splatting method has achieved the state-of-the-art rendering quality and speed combining the benefits of both primitive-based representations and volumetric representations. However, it often leads to heavily redundant Gaussians that try to fit every training view, neglecting the underlying scene geometry. Consequently, the resulting model becomes less robust to significant view changes, texture-less area and lighting effects. We introduce Scaffold-GS, which uses anchor points to distribute local 3D Gaussians, and predicts their attributes on-the-fly based on viewing direction and distance within the view frustum. Anchor growing and pruning strategies are developed based on the importance of neural Gaussians to reliably improve the scene coverage. We show that our method effectively reduces redundant Gaussians while delivering high-quality rendering. We also demonstrates an enhanced capability to accommodate scenes with varying levels-of-detail and view-dependent observations, without sacrificing the rendering speed.

Related papers

• Decoupling Appearance Variations with 3D Consistent Features in Gaussian Splatting [50.98884579463359]
  We propose DAVIGS, a method that decouples appearance variations in a plug-and-play manner. By transforming the rendering results at the image level instead of the Gaussian level, our approach can model appearance variations with minimal optimization time and memory overhead. We validate our method on several appearance-variant scenes, and demonstrate that it achieves state-of-the-art rendering quality with minimal training time and memory usage.
  arXiv  Detail & Related papers  (2025-01-18T14:55:58Z)
• 3D Gaussian Splatting with Normal Information for Mesh Extraction and Improved Rendering [8.59572577251833]
  We propose a novel regularization method using the gradients of a signed distance function estimated from the Gaussians. We demonstrate the effectiveness of our approach on datasets such as Mip-NeRF360, Tanks and Temples, and Deep-Blending.
  arXiv  Detail & Related papers  (2025-01-14T18:40:33Z)
• DehazeGS: Seeing Through Fog with 3D Gaussian Splatting [17.119969983512533]
  We introduce DehazeGS, a method capable of decomposing and rendering a fog-free background from participating media. Experiments on both synthetic and real-world foggy datasets demonstrate that DehazeGS achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-01-07T09:47:46Z)
• Beyond Gaussians: Fast and High-Fidelity 3D Splatting with Linear Kernels [51.08794269211701]
  We introduce 3D Linear Splatting (3DLS), which replaces Gaussian kernels with linear kernels to achieve sharper and more precise results. 3DLS demonstrates state-of-the-art fidelity and accuracy, along with a 30% FPS improvement over baseline 3DGS.
  arXiv  Detail & Related papers  (2024-11-19T11:59:54Z)
• Wild-GS: Real-Time Novel View Synthesis from Unconstrained Photo Collections [30.321151430263946]
  This paper presents Wild-GS, an innovative adaptation of 3DGS optimized for unconstrained photo collections. Wild-GS determines the appearance of each 3D Gaussian by their inherent material attributes, global illumination and camera properties per image, and point-level local variance of reflectance. This novel design effectively transfers the high-frequency detailed appearance of the reference view to 3D space and significantly expedites the training process.
  arXiv  Detail & Related papers  (2024-06-14T19:06:07Z)
• 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)
• Octree-GS: Towards Consistent Real-time Rendering with LOD-Structured 3D Gaussians [18.774112672831155]
  3D-GS has shown remarkable rendering fidelity and efficiency compared to NeRF-based neural scene representations. We introduce Octree-GS, featuring an LOD-structured 3D Gaussian approach supporting level-of-detail decomposition for scene representation.
  arXiv  Detail & Related papers  (2024-03-26T17:39:36Z)
• SWAG: Splatting in the Wild images with Appearance-conditioned Gaussians [2.2369578015657954]
  Implicit neural representation methods have shown impressive advancements in learning 3D scenes from unstructured in-the-wild photo collections. We introduce a new mechanism to train transient Gaussians to handle the presence of scene occluders in an unsupervised manner.
  arXiv  Detail & Related papers  (2024-03-15T16:00:04Z)
• 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)
• Spec-Gaussian: Anisotropic View-Dependent Appearance for 3D Gaussian Splatting [55.71424195454963]
  Spec-Gaussian is an approach that utilizes an anisotropic spherical Gaussian appearance field instead of spherical harmonics. Our experimental results demonstrate that our method surpasses existing approaches in terms of rendering quality. This improvement extends the applicability of 3D GS to handle intricate scenarios with specular and anisotropic surfaces.
  arXiv  Detail & Related papers  (2024-02-24T17:22:15Z)

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.