A Simple And Effective Filtering Scheme For Improving Neural Fields

Understand

Recently, neural fields, also known as coordinate-based MLPs, have achieved impressive results in representing low-dimensional data.

• Unlike CNN, MLPs are globally connected and lack local control; adjusting a local region leads to global changes.
• Therefore, improving local neural fields usually leads to a dilemma: filtering out local artifacts can simultaneously smooth away desired details.
• Our solution is a new filtering technique that consists of two counteractive operators: a smoothing operator that provides global smoothing for better generalization, and conversely a recovering operator that provides better controllability for local adjustments.