综述动态场景重建最新进展，聚焦神经辐射场与3D高斯泼溅方法。
Dynamic Scene Reconstruction: Recent Advance in Real-time Rendering and Streaming
- 按核心原理分类现有方法，梳理主流技术路线。
- 涵盖170余篇论文，对比多个基准数据集表现。
- 适合关注实时渲染与动态场景建模的研究者。
从2D图像重建并渲染动态场景是计算机视觉与图形学中的基础但极具挑战性的问题。本综述系统回顾了动态场景表示与渲染的演进与最新进展，重点关注基于神经辐射场（Neural Radiance Fields）和3D高斯泼溅（3D Gaussian Splatting）的重建方法。我们对现有方法进行系统归纳，按核心原理分类，整理相关数据集，比较各类方法在基准上的性能表现，并探讨该领域面临的挑战与未来研究方向。全文共涵盖170余篇相关论文，全面呈现该领域的最新技术水平。
原文摘要 · Abstract (English)
Representing and rendering dynamic scenes from 2D images is a fundamental yet challenging problem in computer vision and graphics. This survey provides a comprehensive review of the evolution and advancements in dynamic scene representation and rendering, with a particular emphasis on recent progress in Neural Radiance Fields based and 3D Gaussian Splatting based reconstruction methods. We systematically summarize existing approaches, categorize them according to their core principles, compile relevant datasets, compare the performance of various methods on these benchmarks, and explore the challenges and future research directions in this rapidly evolving field. In total, we review over 170 relevant papers, offering a broad perspective on the state of the art in this domain.
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