Filtering In Neural Implicit Functions

Neural implicit functions are highly effective for representing many kinds of data, including images and 3D shapes. However, the implicit functions learned by neural networks usually include over-smoothed patches or noisy artifacts into the results if the data has many scales of details or a wide range of frequencies. Adapting the result containing both noise and over-smoothed regions usually suffers from either over smoothing or noisy issues. To overcome this challenge, we propose a new framework, coined FINN, that integrates a filtering module into the neural network to perform data generation while filtering artifacts. The filtering module has a smoothing operator that acts on the intermediate results of the network and a recovering operator that brings distinct details from the input back to the regions overly smoothed. The proposed method significantly alleviates over smoothing or noisy issues. We demonstrate the advantage of the FINN on the image regression task, considering both real-world and synthetic images, and showcases significant improvement on both quantitative and qualitative results compared to state-of-the-art methods. Moreover, FINN yields better performance in both convergence speed and network stability. Source code is available at https://github.com/yixin26/FINN.