摘要
神经辐射场(NeRF)是一种面向三维隐式空间建模的深度学习模型,在表示和渲染三维场景领域具有重要价值。然而由于神经辐射场算法训练过程复杂、需要大量的计算资源和时间等,其可用性和实用性受到一定限制,如何针对神经辐射场的痛点问题进行优化是当前计算机视觉等领域研究的热点之一。此研究旨在对神经辐射场的优化和应用进行全面综述。首先,在深入解析神经辐射场基本原理的基础上,从渲染质量、计算复杂度、位姿等方面对现阶段神经辐射场的优化情况进行概述;其次,列举神经辐射场应用状况,为未来更高效和实用的算法优化设计提供参考;最后,总结神经辐射场的优势与局限性,并提出未来可能的发展方向,以期发挥神经辐射场在三维渲染、场景合成等方面的巨大潜力。
NeRF is a deep learning model aimed at modeling three-dimensional implicit spaces,and it holds significant value in the representation and rendering of 3D scenes.However,due to the complex training process,substantial computational resources,and time requirements,the usability and practicality of the NeRF algorithm are somewhat limited.Addressing the pain points of NeRF optimization has become a hot topic in the field of computer vision.This paper aimed to provide a comprehensive review of the optimization and application of NeRF.Firstly,it delved into the basic principles of NeRF and outlined the current optimization status from the perspectives of rendering quality,computational complexity,and pose.Secondly,it enumerated the application scenarios of NeRF to provide references for future,more efficient and practical algorithmic optimizations.Finally,it summarized the strengths and limitations of NeRF and proposed potential future directions tailored to harness the tremendous potential of NeRF in 3D rendering,scene synthesis,and beyond.
作者
韩开
徐娟
Han Kai;Xu Juan(School of Information Science,Beijing Language University,Beijing 100083,China)
出处
《计算机应用研究》
CSCD
北大核心
2024年第8期2252-2260,共9页
Application Research of Computers
基金
北京语言大学校级重大资助项目(23ZDY02)
北京语言大学研究生创新基金(中央高校基本科研业务费专项资金)资助项目(24YCX020)
北京语言大学教育基金会创新实践项目。
关键词
神经辐射场
神经渲染
三维场景
深度学习
neural radiance fields(NeRF)
neural rendering
3D scene
deep learning
