Neural fields excel at representing continuous visual signals but typically operate at a single, fixed resolution. We present a simple yet powerful method to optimize neural fields that can be prefiltered in a single forward pass. Key innovations and features include: (1) We perform convolutional filtering in the input domain by analytically scaling Fourier feature embeddings with the filter's frequency response. (2) This closed-form modulation generalizes beyond Gaussian filtering and supports other parametric filters (Box and Lanczos) that are unseen at training time. (3) We train the neural field using single-sample Monte Carlo estimates of the filtered signal. Our method is fast during both training and inference, and imposes no additional constraints on the network architecture. We show quantitative and qualitative improvements over existing methods for neural-field filtering.

翻译：神经场在表示连续视觉信号方面表现出色，但通常以单一固定分辨率运行。我们提出了一种简单而强大的方法，用于优化可在单次前向传播中进行预滤波的神经场。关键创新与特性包括：（1）我们通过在输入域中执行卷积滤波，利用滤波器的频率响应对傅里叶特征嵌入进行解析缩放。（2）这种闭式调制方法不仅适用于高斯滤波，还可推广至训练时未见的其他参数化滤波器（如Box与Lanczos滤波器）。（3）我们使用滤波信号的单样本蒙特卡洛估计来训练神经场。该方法在训练和推理阶段均具有高效性，且不对网络架构施加额外约束。我们通过定量与定性实验证明，本方法在神经场滤波任务上优于现有方法。