3D Gaussian splatting (GS) has emerged as a transformative technique in radiance fields. Unlike mainstream implicit neural models, 3D GS uses millions of learnable 3D Gaussians for an explicit scene representation. Paired with a differentiable rendering algorithm, this approach achieves real-time rendering and unprecedented editability, making it a potential game-changer for 3D reconstruction and representation. In the present paper, we provide the first systematic overview of the recent developments and critical contributions in 3D GS. We begin with a detailed exploration of the underlying principles and the driving forces behind the emergence of 3D GS, laying the groundwork for understanding its significance. A focal point of our discussion is the practical applicability of 3D GS. By enabling unprecedented rendering speed, 3D GS opens up a plethora of applications, ranging from virtual reality to interactive media and beyond. This is complemented by a comparative analysis of leading 3D GS models, evaluated across various benchmark tasks to highlight their performance and practical utility. The survey concludes by identifying current challenges and suggesting potential avenues for future research. Through this survey, we aim to provide a valuable resource for both newcomers and seasoned researchers, fostering further exploration and advancement in explicit radiance field.

翻译：三维高斯溅射(3D GS)已成为辐射场领域的一项变革性技术。与主流隐式神经模型不同，3D GS利用数百万个可学习的3D高斯体进行显式场景表示。结合可微分渲染算法，该方法实现了实时渲染和前所未有的可编辑性，使其成为3D重建与表示领域的潜在颠覆性技术。本文首次系统梳理了3D GS领域的最新进展与关键贡献。我们首先深入探讨3D GS的基本原理及其涌现驱动力，为理解其重要性奠定基础。讨论焦点集中于3D GS的实际应用价值——通过实现空前的渲染速度，3D GS开辟了从虚拟现实到交互式媒体等广泛的应用场景。通过跨基准任务的对比分析，我们评估了主流3D GS模型的性能表现与实用价值。本综述最终指出现存挑战并展望未来研究方向，旨在为新手与资深研究者提供宝贵资源，推动显式辐射场领域的深入探索与发展。