We present a novel method to provide efficient and highly detailed reconstructions. Inspired by wavelets, our main idea is to 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 efficiency on multiple tasks: 2D image fitting, 3D shape reconstruction, and neural radiance fields.
翻译:我们展示了一种创新的方法来提供高效和高度详细的重建。 在波子的启发下, 我们的主要想法是学习一个在空间和频率上分解信号的神经场。 我们遵循最近的基于网格的空间分解范式, 但与现有工作不同, 我们鼓励通过 Fourier 特征编码将特定频率存储在每一个网格中。 然后我们应用一个带有正弦活化的多层感应器, 在适当的层中使用这些四维编码的特性, 以便高频组件在低频组件上依次堆积, 我们总结得出最终产出。 我们证明我们的方法超过了关于多个任务( 2D 图像安装, 3D 形状重建, 和 神经光谱场) 的艺术状态 。