SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering

• URL: http://arxiv.org/abs/2311.12775v3
• Date: Sat, 2 Dec 2023 16:19:12 GMT
• Title: SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering
• Authors: Antoine Gu\'edon and Vincent Lepetit
• Abstract summary: We propose a method to allow precise and extremely fast mesh extraction from 3D Gaussian Splatting. It is however challenging to extract a mesh from the millions of tiny 3D gaussians as these gaussians tend to be unorganized after optimization.
• Score: 24.91019554830571
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a method to allow precise and extremely fast mesh extraction from 3D Gaussian Splatting. Gaussian Splatting has recently become very popular as it yields realistic rendering while being significantly faster to train than NeRFs. It is however challenging to extract a mesh from the millions of tiny 3D gaussians as these gaussians tend to be unorganized after optimization and no method has been proposed so far. Our first key contribution is a regularization term that encourages the gaussians to align well with the surface of the scene. We then introduce a method that exploits this alignment to extract a mesh from the Gaussians using Poisson reconstruction, which is fast, scalable, and preserves details, in contrast to the Marching Cubes algorithm usually applied to extract meshes from Neural SDFs. Finally, we introduce an optional refinement strategy that binds gaussians to the surface of the mesh, and jointly optimizes these Gaussians and the mesh through Gaussian splatting rendering. This enables easy editing, sculpting, rigging, animating, compositing and relighting of the Gaussians using traditional softwares by manipulating the mesh instead of the gaussians themselves. Retrieving such an editable mesh for realistic rendering is done within minutes with our method, compared to hours with the state-of-the-art methods on neural SDFs, while providing a better rendering quality. Our project page is the following: https://anttwo.github.io/sugar/

Related papers

• Gaussian-Forest: Hierarchical-Hybrid 3D Gaussian Splatting for Compressed Scene Modeling [20.705053444057835]
  We introduce the Gaussian-Forest modeling framework, which hierarchically represents a scene as a forest of hybrid 3D Gaussians. Experiments demonstrate that Gaussian-Forest not only maintains comparable speed and quality but also achieves a compression rate surpassing 10 times.
  arXiv  Detail & Related papers  (2024-06-13T02:41:11Z)
• RaDe-GS: Rasterizing Depth in Gaussian Splatting [32.38730602146176]
  Gaussian Splatting (GS) has proven to be highly effective in novel view synthesis, achieving high-quality and real-time rendering. Our work introduces a Chamfer distance error comparable to NeuraLangelo on the DTU dataset and maintains similar computational efficiency as the original 3D GS methods.
  arXiv  Detail & Related papers  (2024-06-03T15:56:58Z)
• RTG-SLAM: Real-time 3D Reconstruction at Scale using Gaussian Splatting [51.51310922527121]
  We present a real-time 3D reconstruction system with an RGBD camera for large-scale environments using Gaussian splatting. We force each Gaussian to be either opaque or nearly transparent, with the opaque ones fitting the surface and dominant colors, and transparent ones fitting residual colors. We show real-time reconstructions of a variety of large scenes and show superior performance in the realism of novel view synthesis and camera tracking accuracy.
  arXiv  Detail & Related papers  (2024-04-30T16:54:59Z)
• Gaussian Opacity Fields: Efficient and Compact Surface Reconstruction in Unbounded Scenes [50.92217884840301]
  Gaussian Opacity Fields (GOF) is a novel approach for efficient, high-quality, and compact surface reconstruction in scenes. GOF is derived from ray-tracing-based volume rendering of 3D Gaussians. GOF surpasses existing 3DGS-based methods in surface reconstruction and novel view synthesis.
  arXiv  Detail & Related papers  (2024-04-16T17:57:19Z)
• 3D Gaussian Splatting as Markov Chain Monte Carlo [30.04096439325343]
  3D Gaussian Splatting has recently become popular for neural rendering. We rethink the set of 3D Gaussians as a random sample drawn from an underlying probability distribution. We introduce a regularizer that promotes the removal of unused Gaussians.
  arXiv  Detail & Related papers  (2024-04-15T09:01:47Z)
• Bridging 3D Gaussian and Mesh for Freeview Video Rendering [57.21847030980905]
  GauMesh bridges the 3D Gaussian and Mesh for modeling and rendering the dynamic scenes. We show that our approach adapts the appropriate type of primitives to represent the different parts of the dynamic scene.
  arXiv  Detail & Related papers  (2024-03-18T04:01:26Z)
• 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)
• GaMeS: Mesh-Based Adapting and Modification of Gaussian Splatting [11.791944275269266]
  We introduce the Gaussian Mesh Splatting (GaMeS) model, which allows modification of Gaussian components in a similar way as meshes. We also define Gaussian splats solely based on their location on the mesh, allowing for automatic adjustments in position, scale, and rotation during animation.
  arXiv  Detail & Related papers  (2024-02-02T14:50:23Z)
• NeuSG: Neural Implicit Surface Reconstruction with 3D Gaussian Splatting Guidance [59.08521048003009]
  We propose a neural implicit surface reconstruction pipeline with guidance from 3D Gaussian Splatting to recover highly detailed surfaces. The advantage of 3D Gaussian Splatting is that it can generate dense point clouds with detailed structure. We introduce a scale regularizer to pull the centers close to the surface by enforcing the 3D Gaussians to be extremely thin.
  arXiv  Detail & Related papers  (2023-12-01T07:04:47Z)
• GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting [51.96353586773191]
  We introduce textbfGS-SLAM that first utilizes 3D Gaussian representation in the Simultaneous Localization and Mapping system. Our method utilizes a real-time differentiable splatting rendering pipeline that offers significant speedup to map optimization and RGB-D rendering. Our method achieves competitive performance compared with existing state-of-the-art real-time methods on the Replica, TUM-RGBD datasets.
  arXiv  Detail & Related papers  (2023-11-20T12:08:23Z)
• DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation [55.661467968178066]
  We propose DreamGaussian, a novel 3D content generation framework that achieves both efficiency and quality simultaneously. Our key insight is to design a generative 3D Gaussian Splatting model with companioned mesh extraction and texture refinement in UV space. In contrast to the occupancy pruning used in Neural Radiance Fields, we demonstrate that the progressive densification of 3D Gaussians converges significantly faster for 3D generative tasks.
  arXiv  Detail & Related papers  (2023-09-28T17:55:05Z)

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.