We present a novel method to provide efficient and highly detailed reconstructions. Inspired by wavelets, we learn a neural field that decompose the signal both spatially and frequency-wise. We follow the recent grid-based paradigm for spatial decomposition, but unlike existing work, encourage specific frequencies to be stored in each grid via Fourier features encodings. We then apply a multi-layer perceptron with sine activations, taking these Fourier encoded features in at appropriate layers so that higher-frequency components are accumulated on top of lower-frequency components sequentially, which we sum up to form the final output. We demonstrate that our method outperforms the state of the art regarding model compactness and convergence speed on multiple tasks: 2D image fitting, 3D shape reconstruction, and neural radiance fields. Our code is available at https://github.com/ubc-vision/NFFB.

翻译：我们提出了一种能够实现高效且高细节重建的新方法。受小波变换启发，我们学习了一个可在空间域和频率域上对信号进行分解的神经场。我们沿用了近期基于网格的空间分解范式，但与现有工作不同，我们通过傅里叶特征编码在每个网格中存储特定频率。随后，我们应用一个包含正弦激活的多层感知机，在适当层级输入这些傅里叶编码的特征，使高频分量依次叠加在低频分量之上，并将其求和形成最终输出。我们证明，在模型紧凑性和收敛速度方面，我们的方法在多项任务（二维图像拟合、三维形状重建以及神经辐射场）上均优于现有技术。我们的代码已开源至 https://github.com/ubc-vision/NFFB。