综述神经辐射场在真实世界中的进展与挑战
Neural Radiance Fields for the Real World: A Survey
- 系统梳理NeRF理论创新与替代表示方法
- 总结重建、计算机视觉、机器人等应用场景
- 适合关注3D视觉与生成技术的研究者阅读
神经辐射场（NeRF）自发布以来重塑了3D场景建模方式，能够从2D图像有效重建复杂3D场景，推动了场景理解、3D内容生成和机器人等多个领域的发展。尽管研究进展显著，但对近期创新、应用及挑战的全面回顾仍显不足。本综述整理了关键理论进展与替代场景表示方法，探讨新兴挑战，深入分析重建应用，揭示NeRF在计算机视觉与机器人中的影响，并梳理重要数据集与工具包。通过识别文献空白，本文讨论开放性问题，提出未来研究方向。
原文摘要 · Abstract (English)
Neural Radiance Fields (NeRFs) have remodeled 3D scene representation since release. NeRFs can effectively reconstruct complex 3D scenes from 2D images, advancing different fields and applications such as scene understanding, 3D content generation, and robotics. Despite significant research progress, a thorough review of recent innovations, applications, and challenges is lacking. This survey compiles key theoretical advancements and alternative scene representations and investigates emerging challenges. It further explores applications on reconstruction, highlights NeRFs' impact on computer vision and robotics, and reviews essential datasets and toolkits. By identifying gaps in the literature, this survey discusses open challenges and offers directions for future research.
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