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March 17, 20240 citationsOpen Access

Recent Advances in 3D Gaussian Splatting

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TWTong WuYYYujie YuanLZLingxiao Zhang

Key Points

  • The 3D Gaussian Splatting method greatly accelerates rendering speed while enabling efficient novel view synthesis.
  • Key performance metrics indicate substantial improvements over neural implicit methods like Neural Radiance Fields.
  • Literature review explores traditional point-based rendering alongside recent advancements in 3D reconstruction and editing techniques for rapid application development in industry and research environments. The explicit representation allows for effective geometry editing and dynamic reconstruction of scenes, enhancing usability across various applications.

Abstract

The emergence of 3D Gaussian Splatting (3DGS) has greatly accelerated the rendering speed of novel view synthesis. Unlike neural implicit representations like Neural Radiance Fields (NeRF) 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 the fast rendering speed, the explicit representation of 3D Gaussian Splatting facilitates editing tasks like dynamic reconstruction, geometry editing, and physical simulation. Considering the rapid change and growing number of works in this field, 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 by functionality. Traditional point-based rendering methods and the rendering formulation of 3D Gaussian Splatting are also illustrated for a better understanding of this technique. This survey aims to help beginners get into this field quickly and provide experienced researchers with a comprehensive overview, which can stimulate the future development of the 3D Gaussian Splatting representation.

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Cite This Study

Wu et al. (2024) studied this question.

synapsesocial.com/papers/68e73a8db6db6435876b4942https://doi.org/10.48550/arxiv.2403.11134
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