3D Gaussian Splatting (3D-GS) has emerged as a significant advancement in the field of Computer Graphics, offering explicit scene representation and novel view synthesis without the reliance on neural networks, such as Neural Radiance Fields (NeRF). This technique has found diverse applications in areas such as robotics, urban mapping, autonomous navigation, and virtual reality/augmented reality, just name a few. Given the growing popularity and expanding research in 3D Gaussian Splatting, this paper presents a comprehensive survey of relevant papers from the past year. We organize the survey into taxonomies based on characteristics and applications, providing an introduction to the theoretical underpinnings of 3D Gaussian Splatting. Our goal through this survey is to acquaint new researchers with 3D Gaussian Splatting, serve as a valuable reference for seminal works in the field, and inspire future research directions, as discussed in our concluding section.

翻译：3D高斯泼溅（3D-GS）已成为计算机图形学领域的一项重大进展，它提供了无需依赖神经网络（如神经辐射场，NeRF）的显式场景表示和新视角合成技术。该技术在机器人、城市地图构建、自主导航以及虚拟现实/增强现实等多个领域得到了广泛应用。鉴于3D高斯泼溅日益增长的受欢迎程度和不断扩展的研究，本文对过去一年的相关论文进行了全面综述。我们基于特性和应用将综述组织成分类体系，并介绍了3D高斯泼溅的理论基础。通过本综述，我们旨在让新研究者了解3D高斯泼溅，为该领域的开创性工作提供有价值的参考，并激发未来研究方向，如结论部分所述。