DWTNeRF: Boosting Few-shot Neural Radiance Fields via Discrete Wavelet Transform

Understand

Neural Radiance Fields (NeRF) has achieved superior performance in novel view synthesis and 3D scene representation, but its practical applications are hindered by slow convergence and reliance on dense training views.

• To this end, we present DWTNeRF, a unified framework based on Instant-NGP's fast-training hash encoding.
• It is coupled with regularization terms designed for few-shot NeRF, which operates on sparse training views.
• Our DWTNeRF additionally includes a novel Discrete Wavelet loss that allows explicit prioritization of low frequencies directly in the training objective, reducing few-shot NeRF's overfitting on high frequencies in earlier training stages.