arxiv_cv 90% Match Research Paper Computer Graphics Researchers,AI Researchers,ML Engineers,Game Developers 3 weeks ago

Spectral Prefiltering of Neural Fields

computer-vision › 3d-vision

📄 Abstract

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.

Key Contributions

Presents a method to optimize neural fields that can be prefiltered in a single forward pass by analytically scaling Fourier feature embeddings with filter frequency responses. This allows for efficient convolutional filtering in the input domain, generalizing beyond Gaussian filters and supporting unseen parametric filters.

Business Value

Enables more efficient and flexible rendering and manipulation of neural field representations, useful in graphics, VR/AR, and simulation.

Paper Metadata

Innovation Type

New filtering technique for neural fields

Deployment Feasibility

Feasible for integration into graphics rendering pipelines and neural field applications. The method is computationally efficient.

Limitations Addressed

Neural fields typically operate at a single, fixed resolution and lack efficient, generalizable filtering mechanisms.

Performance Gains

Quantitative and qualitative improvements over existing methods for neural-field filtering, with fast training and inference.

Technical Tags

neural fieldsspectral filteringFourier featuresconvolutional filteringfrequency responseparametric filtersMonte Carlo estimationreal-time inferenceinput domain filtering

Research Topics

Neural FieldsComputer GraphicsSignal ProcessingDeep LearningRendering

Methods & Architectures

Analytical scaling of Fourier feature embeddingsConvolutional filtering in input domainSingle-sample Monte Carlo estimationParametric filter generalization Neural Fields

Applications & Tasks

Computer Graphics Image Processing 3D Scene Representation Rendering Enabling efficient filtering of neural fieldsOptimizing neural fields for prefiltered signalsGeneralizing filtering to unseen parametric filters Applying spectral filters to neural fieldsGenerating prefiltered neural field representationsImproving rendering quality and efficiency

Related Fields

Computer GraphicsComputer VisionDeep LearningSignal ProcessingMachine Learning

Keywords

Neural FieldsSpectral FilteringFourier FeaturesConvolutional FilteringInput Domain FilteringParametric FiltersNeural RenderingComputer GraphicsSignal ProcessingDeep Learning

Academic Context

#Neural Fields#Computer Graphics#Signal Processing#Deep Learning#Rendering

Commercial Potential

Potential Products

Graphics rendering librariesTools for manipulating neural representationsEfficient neural field implementations

Target Industries

GamingFilm & EntertainmentVirtual RealityAugmented RealitySoftware Development

Use Case Examples

Applying various filters (e.g., blur, sharpen) to neural field representations efficientlyGenerating prefiltered neural fields for faster renderingCreating novel visual effects using spectral filtering

Competitive Edge

Provides a novel and efficient method for spectral filtering of neural fields, overcoming limitations of fixed-resolution representations.

Market Opportunity

Growing market for advanced rendering techniques and neural representations.

Revenue Models

Licensing of algorithmsintegration into graphics software.

Resource Requirements

Compute Needs

Efficient during training and inference due to analytical solutions.

Data Requirements

Data suitable for training neural fields (e.g., images, scene data).

Deployment Constraints

Requires neural field representations that utilize Fourier features.

Scalability

The method is computationally efficient, suggesting good scalability.

Production Readiness

Maturity Level

Research Prototype

Time to Market

1-2 years for integration into graphics engines.

Patent Potential

Moderate, related to the analytical filtering technique for neural fields.

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