Content-Aware Frequency Encoding for Implicit Neural Representations with Fourier-Chebyshev Features

Researchers propose Content-Aware Frequency Encoding (CAFE), a new method for Implicit Neural Representations that addresses spectral bias limitations through adaptive frequency selection. The technique uses parallel linear layers with Hadamard products and extends to CAFE+ with Chebyshev features, demonstrating superior performance across multiple benchmarks.

• →CAFE addresses the spectral bias problem in Implicit Neural Representations that limits high-frequency detail capture.
• →The method uses multiple parallel linear layers combined via Hadamard product to synthesize broader frequency bases.
• →CAFE+ incorporates Chebyshev features alongside Fourier bases for enhanced stability and representation.
• →Extensive benchmarks show consistent superior performance over existing methods.
• →The approach enables more efficient frequency composition compared to traditional fixed frequency bases.