论文标题
神经傅里叶过滤库
Neural Fourier Filter Bank
论文作者
论文摘要
我们提出了一种提供高效且高度详细的重建的新方法。受小波的启发，我们学习了一个神经场，该神经场在空间和频率方面都分解了信号。我们遵循最近基于网格的范式进行空间分解，但与现有工作不同，我们鼓励通过傅立叶特征编码在每个网格中存储特定的频率。然后，我们将具有正弦激活的多层感知器应用于适当的层中，以将这些傅立叶编码的特征在适当的层中添加到较低的低频组件的顶部，然后将其顺序累积，我们将其总结为最终输出。我们证明，我们的方法在多个任务上超过了模型紧凑和收敛速度的最新技术：2D图像拟合，3D形状重建和神经辐射场。我们的代码可在https://github.com/ubc-vision/nffb上找到。
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.
