Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenes
- URL: http://arxiv.org/abs/2404.10772v2
- Date: Wed, 11 Sep 2024 08:53:27 GMT
- Title: Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenes
- Authors: Zehao Yu, Torsten Sattler, Andreas Geiger,
- Abstract summary: Gaussian Opacity Fields (GOF) is a novel approach for efficient, high-quality, and adaptive 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.
- Score: 50.92217884840301
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recently, 3D Gaussian Splatting (3DGS) has demonstrated impressive novel view synthesis results, while allowing the rendering of high-resolution images in real-time. However, leveraging 3D Gaussians for surface reconstruction poses significant challenges due to the explicit and disconnected nature of 3D Gaussians. In this work, we present Gaussian Opacity Fields (GOF), a novel approach for efficient, high-quality, and adaptive surface reconstruction in unbounded scenes. Our GOF is derived from ray-tracing-based volume rendering of 3D Gaussians, enabling direct geometry extraction from 3D Gaussians by identifying its levelset, without resorting to Poisson reconstruction or TSDF fusion as in previous work. We approximate the surface normal of Gaussians as the normal of the ray-Gaussian intersection plane, enabling the application of regularization that significantly enhances geometry. Furthermore, we develop an efficient geometry extraction method utilizing Marching Tetrahedra, where the tetrahedral grids are induced from 3D Gaussians and thus adapt to the scene's complexity. Our evaluations reveal that GOF surpasses existing 3DGS-based methods in surface reconstruction and novel view synthesis. Further, it compares favorably to or even outperforms, neural implicit methods in both quality and speed.
Related papers
- Effective Rank Analysis and Regularization for Enhanced 3D Gaussian Splatting [33.01987451251659]
3D Gaussian Splatting (3DGS) has emerged as a promising technique capable of real-time rendering with high-quality 3D reconstruction.
Despite its potential, 3DGS encounters challenges, including needle-like artifacts, suboptimal geometries, and inaccurate normals.
We introduce effective rank as a regularization, which constrains the structure of the Gaussians.
arXiv Detail & Related papers (2024-06-17T15:51:59Z) - 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) - R$^2$-Gaussian: Rectifying Radiative Gaussian Splatting for Tomographic Reconstruction [53.19869886963333]
3D Gaussian splatting (3DGS) has shown promising results in rendering image and surface reconstruction.
This paper introduces R2$-Gaussian, the first 3DGS-based framework for sparse-view tomographic reconstruction.
arXiv Detail & Related papers (2024-05-31T08:39:02Z) - GaussianRoom: Improving 3D Gaussian Splatting with SDF Guidance and Monocular Cues for Indoor Scene Reconstruction [3.043712258792239]
We present a unified framework integrating neural SDF with 3DGS.
This framework incorporates a learnable neural SDF field to guide the densification and pruning of Gaussians.
Our method achieves state-of-the-art performance in both surface reconstruction and novel view synthesis.
arXiv Detail & Related papers (2024-05-30T03:46:59Z) - 3DGSR: Implicit Surface Reconstruction with 3D Gaussian Splatting [58.95801720309658]
In this paper, we present an implicit surface reconstruction method with 3D Gaussian Splatting (3DGS), namely 3DGSR.
The key insight is incorporating an implicit signed distance field (SDF) within 3D Gaussians to enable them to be aligned and jointly optimized.
Our experimental results demonstrate that our 3DGSR method enables high-quality 3D surface reconstruction while preserving the efficiency and rendering quality of 3DGS.
arXiv Detail & Related papers (2024-03-30T16:35:38Z) - 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) - GaussianPro: 3D Gaussian Splatting with Progressive Propagation [49.918797726059545]
3DGS relies heavily on the point cloud produced by Structure-from-Motion (SfM) techniques.
We propose a novel method that applies a progressive propagation strategy to guide the densification of the 3D Gaussians.
Our method significantly surpasses 3DGS on the dataset, exhibiting an improvement of 1.15dB in terms of PSNR.
arXiv Detail & Related papers (2024-02-22T16:00:20Z) - 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)
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.