Year: 2025
Type: article
Abstract: Gaussian Splatting has emerged as a powerful technique for signal representation, especially in 3D. This paper introduces Gaussian Splatting and demonstrates its application across 1D, 2D, and 3D cases. We also discuss Gaussian Splatting in relation to Neural Radiance Fields (NeRF), highlighting the computational trade-offs and performance benefits. Through this work, we aim to bridge the gap between foundational concepts in view synthesis and advanced research, making Gaussian Splatting a more approachable and widely understood technique in the field of signal processing and computer vision. We provide code examples and detailed explanations to make the topic accessible to a broader audience, enabling readers to dive into more advanced technical papers with ease. **This is an MLBriefs article, the source code has not been reviewed!** **The original source codes are available here: [[2D Gaussian Splatting|https://github.com/OutofAi/2D-Gaussian-Splatting]] and [[Gaussian Splatting|https://github.com/nerfstudio-project/gsplat]] (last checked 2025/06/17).**
Source: Image Processing On Line
Authors: Akash Malhotra, Nacéra Seghouani
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4411368884