Recent advances in 3D Gaussian splatting

By Tong Wu, Yu-Jie Yuan, Ling-Xiao Zhang, Jie Yang, Yan-Pei Cao, Ling-Qi Yan, Lin Gao. Computational Visual Media, 2024

297 citations recorded by the source corpus.

The emergence of 3D Gaussian splatting (3DGS) has greatly accelerated rendering in novel view synthesis. Unlike neural implicit representations like neural radiance fields (NeRFs) that represent a 3D scene with position and viewpoint-conditioned neural networks, 3D Gaussian splatting utilizes a set of Gaussian ellipsoids to model the scene so that efficient rendering can be accomplished by rasterizing Gaussian ellipsoids into images. Apart from fast rendering, the explicit representation of 3D Gaussian splatting also facilitates downstream tasks like dynamic reconstruction, geometry editing, and physical simulation. Considering the rapid changes and growing number of works in this field, we present a literature review of recent 3D Gaussian splatting methods, which can be roughly classified by functionality into 3D reconstruction, 3D editing, and other downstream applications. Traditional point-based rendering methods and the rendering formulation of 3D Gaussian splatting are also covered to aid understanding of this technique. This survey aims to help beginners to quickly get started in this field and to provide experienced researchers with a comprehensive overview, aiming to stimulate future development of the 3D Gaussian splatting representation.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/90607c8d66c9e397d697214f326560231f7834ba

Citations in the corpus

• SC-GS: Sparse-Controlled Gaussian Splatting for Editable Dynamic Scenes (2023, unknown): “Gaussian Splatting [13, 51] offers improved rendering quality and speed for radiance fields.”
• gsplat: An Open-Source Library for Gaussian Splatting (2024, unknown): “Gaussian Splatting, a seminal work proposed by Kerbl et al. (2023) is a rapidly developing area of research for high fidelity 3D scene reconstruction and novel view synthesis with wide interest in both academia and industry (Fei et al., 2024; Chen and Wang, 2024; Bao et al., 2024; Wu et al., 2024).”
• Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D Reconstruction with Transformers (2023, unknown): “Recently, Gaussian Splat-ting [28, 71] has advanced by employing anisotropic 3D Gaussians for high-quality radiance field representation, improving both novel view synthesis quality and rendering speed through adaptive density control and efficient, differentiable rendering.”
• A Survey on 3D Gaussian Splatting (2024, unknown): “…of this paper from existing surveys (general [11, 41, 52, 267] or domain-specific [5, 10, 75]) are evident in the following aspects: • We provide the first systematic and comprehensive review that examines 3D GS from a macro-level perspective by establishing clear taxonomies and frameworks.”