基于傅里叶频域截断的神经辐射场优化

Neural radiance field optimization based on Fourier frequency domain truncation

神经辐射场(neural radiance fields,NeRF)作为一种通用的场景表达方法,可以更好地理解三维世界的同时创造出更加逼真的感官体验。然而在实际应用中,输入图像较少导致重建效果不佳是一个常见的问题。为此,本文提出了基于傅里叶频域截断的神经辐射场(sparse views neural radiance fields,Sv-NeRF),通过在频域空间对输入频率进行截断并应用正则化策略来控制高频信号的输入来优化NeRF的位置编码机制,有效地降低了高频噪声,保留了关键的细节信息以提升渲染的质量和稳定性。该方法提升了模型对场景的理解能力,相较于现有方法在渲染质量、细节保留能力上均有显著提升,尤其适用于稀疏输入视角的场景重建工作。

Neural radiance field(NeRF),as a general method of scene expression,can better understand the three-dimensional(3D)world and create a more realistic sensory experience.However,in practical application,it is a common problem that less input images lead to poor reconstruction effect.Thus,a sparse-view neural radiance field(Sv-NeRF)is proposed in this study based on Fourier frequency domain truncation.The position coding mechanism of NeRF is optimized by truncating the input frequency in the frequency domain space and applying a regularization strategy to control the input of high-frequency signals.This method effectively reduces high-frequency noise and retains key details to improve the quality and stability of rendering.Compared with other methods,the proposed method improves the ability of the model to understand the scene significantly,and improves the rendering quality and detail preservation ability.It is especially suitable for scene reconstruction from the sparse input perspective.