Related papers: Spectral Prefiltering of Neural Fields

Spectral Prefiltering of Neural Fields

URL: http://arxiv.org/abs/2510.08394v1
Date: Thu, 09 Oct 2025 16:15:46 GMT
Title: Spectral Prefiltering of Neural Fields
Authors: Mustafa B. Yaldiz, Ishit Mehta, Nithin Raghavan, Andreas Meuleman, Tzu-Mao Li, Ravi Ramamoorthi,
Abstract summary: We present a simple yet powerful method to optimize neural fields that can be prefiltered in a single forward pass.<n>We perform convolutional filtering in the input domain by analytically scaling Fourier feature embeddings with the filter's frequency response.<n>We train the neural field using single-sample Monte Carlo estimates of the filtered signal.
Score: 29.748460793696648
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

Related papers

BANF: Band-limited Neural Fields for Levels of Detail Reconstruction [28.95113960996025]
We show that via a simple modification, one can obtain neural fields that are low-pass filtered, and in turn show how this can be exploited to obtain a frequency decomposition of the entire signal. We demonstrate the validity of our technique by investigating level-of-detail reconstruction, and showing how coarser representations can be computed effectively.
arXiv Detail & Related papers (2024-04-19T17:39:50Z)
As large as it gets: Learning infinitely large Filters via Neural Implicit Functions in the Fourier Domain [22.512062422338914]
Recent work in neural networks for image classification has seen a strong tendency towards increasing the spatial context. We propose a module for studying the effective filter size of convolutional neural networks. Our analysis shows that, although the proposed networks could learn very large convolution kernels, the learned filters are well localized and relatively small in practice.
arXiv Detail & Related papers (2023-07-19T14:21:11Z)
Computational Doob's h-transforms for Online Filtering of Discretely Observed Diffusions [65.74069050283998]
We propose a computational framework to approximate Doob's $h$-transforms. The proposed approach can be orders of magnitude more efficient than state-of-the-art particle filters.
arXiv Detail & Related papers (2022-06-07T15:03:05Z)
Deep Learning for the Benes Filter [91.3755431537592]
We present a new numerical method based on the mesh-free neural network representation of the density of the solution of the Benes model. We discuss the role of nonlinearity in the filtering model equations for the choice of the domain of the neural network.
arXiv Detail & Related papers (2022-03-09T14:08:38Z)
Batch Normalization Tells You Which Filter is Important [49.903610684578716]
We propose a simple yet effective filter pruning method by evaluating the importance of each filter based on the BN parameters of pre-trained CNNs. The experimental results on CIFAR-10 and ImageNet demonstrate that the proposed method can achieve outstanding performance.
arXiv Detail & Related papers (2021-12-02T12:04:59Z)
Direct design of biquad filter cascades with deep learning by sampling random polynomials [5.1118282767275005]
In this work, we learn a direct mapping from the target magnitude response to the filter coefficient space with a neural network trained on millions of random filters. We demonstrate our approach enables both fast and accurate estimation of filter coefficients given a desired response. We compare our method against existing methods including modified Yule-Walker and gradient descent and show IIRNet is, on average, both faster and more accurate.
arXiv Detail & Related papers (2021-10-07T17:58:08Z)
Learning Versatile Convolution Filters for Efficient Visual Recognition [125.34595948003745]
This paper introduces versatile filters to construct efficient convolutional neural networks. We conduct theoretical analysis on network complexity and an efficient convolution scheme is introduced. Experimental results on benchmark datasets and neural networks demonstrate that our versatile filters are able to achieve comparable accuracy as that of original filters.
arXiv Detail & Related papers (2021-09-20T06:07:14Z)
Graph Neural Networks with Adaptive Frequency Response Filter [55.626174910206046]
We develop a graph neural network framework AdaGNN with a well-smooth adaptive frequency response filter. We empirically validate the effectiveness of the proposed framework on various benchmark datasets.
arXiv Detail & Related papers (2021-04-26T19:31:21Z)
Filter Grafting for Deep Neural Networks: Reason, Method, and Cultivation [86.91324735966766]
Filter is the key component in modern convolutional neural networks (CNNs) In this paper, we introduce filter grafting (textbfMethod) to achieve this goal. We develop a novel criterion to measure the information of filters and an adaptive weighting strategy to balance the grafted information among networks.
arXiv Detail & Related papers (2020-04-26T08:36:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.