Isotropic Gaussian Splatting for Real-Time Radiance Field Rendering
- URL: http://arxiv.org/abs/2403.14244v1
- Date: Thu, 21 Mar 2024 09:02:31 GMT
- Title: Isotropic Gaussian Splatting for Real-Time Radiance Field Rendering
- Authors: Yuanhao Gong, Lantao Yu, Guanghui Yue,
- Abstract summary: The proposed method can be applied in a large range applications, such as 3D reconstruction, view synthesis, and dynamic object modeling.
The experiments confirm that the proposed method is about bf 100X faster without losing the geometry representation accuracy.
- Score: 15.498640737050412
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The 3D Gaussian splatting method has drawn a lot of attention, thanks to its high performance in training and high quality of the rendered image. However, it uses anisotropic Gaussian kernels to represent the scene. Although such anisotropic kernels have advantages in representing the geometry, they lead to difficulties in terms of computation, such as splitting or merging two kernels. In this paper, we propose to use isotropic Gaussian kernels to avoid such difficulties in the computation, leading to a higher performance method. The experiments confirm that the proposed method is about {\bf 100X} faster without losing the geometry representation accuracy. The proposed method can be applied in a large range applications where the radiance field is needed, such as 3D reconstruction, view synthesis, and dynamic object modeling.
Related papers
- GPS-Gaussian+: Generalizable Pixel-wise 3D Gaussian Splatting for Real-Time Human-Scene Rendering from Sparse Views [67.34073368933814]
We propose a generalizable Gaussian Splatting approach for high-resolution image rendering under a sparse-view camera setting.
We train our Gaussian parameter regression module on human-only data or human-scene data, jointly with a depth estimation module to lift 2D parameter maps to 3D space.
Experiments on several datasets demonstrate that our method outperforms state-of-the-art methods while achieving an exceeding rendering speed.
arXiv Detail & Related papers (2024-11-18T08:18:44Z) - Structure Consistent Gaussian Splatting with Matching Prior for Few-shot Novel View Synthesis [28.3325478008559]
We propose SCGaussian, a Structure Consistent Gaussian Splatting method using matching priors to learn 3D consistent scene structure.
We optimize the scene structure in two folds: rendering geometry and, more importantly, the position of Gaussian primitives.
Experiments on forward-facing, surrounding, and complex large scenes show the effectiveness of our approach with state-of-the-art performance and high efficiency.
arXiv Detail & Related papers (2024-11-06T03:28:06Z) - 2DGH: 2D Gaussian-Hermite Splatting for High-quality Rendering and Better Geometry Reconstruction [7.787937866297091]
2D Gaussian Splatting has recently emerged as a significant method in 3D reconstruction.
We propose to use the Gaussian-Hermite kernel as the new primitive in Gaussian splatting.
Our experiments demonstrate the extraordinary performance of Gaussian-Hermite kernel in both geometry reconstruction and novel-view synthesis tasks.
arXiv Detail & Related papers (2024-08-30T03:04:11Z) - GaussianForest: Hierarchical-Hybrid 3D Gaussian Splatting for Compressed Scene Modeling [40.743135560583816]
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) - 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) - Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenes [50.92217884840301]
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.
arXiv Detail & Related papers (2024-04-16T17:57:19Z) - GaussianCube: A Structured and Explicit Radiance Representation for 3D Generative Modeling [55.05713977022407]
We introduce a radiance representation that is both structured and fully explicit and thus greatly facilitates 3D generative modeling.
We derive GaussianCube by first using a novel densification-constrained Gaussian fitting algorithm, which yields high-accuracy fitting.
Experiments conducted on unconditional and class-conditioned object generation, digital avatar creation, and text-to-3D all show that our model synthesis achieves state-of-the-art generation results.
arXiv Detail & Related papers (2024-03-28T17:59:50Z) - Mesh-based Gaussian Splatting for Real-time Large-scale Deformation [58.18290393082119]
It is challenging for users to directly deform or manipulate implicit representations with large deformations in the real-time fashion.
We develop a novel GS-based method that enables interactive deformation.
Our approach achieves high-quality reconstruction and effective deformation, while maintaining the promising rendering results at a high frame rate.
arXiv Detail & Related papers (2024-02-07T12:36:54Z) - 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)
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.