Generating high-fidelity 3D representations of real-world scenes is becoming more feasible thanks to recent advances in Neural Radiation Field (NeRF) applications. With NeRF, you can transfer scenes from the real world to a virtual world and have 3D renderings that can be viewed from different perspectives.
NeRF is a deep learning based approach that renders the scene as a continuous 5D feature. Maps 3D coordinates and viewing directions to radiation values that represent the amount of light traveling along the given direction at a given point. This radiation function is approximated using a multilayer perceptron (MLP) that is trained on a set of input images and the corresponding camera parameters.
By capturing the underlying 3D geometry and lighting of the scene, NeRF can generate novel views of the scene from arbitrary vantage points. In this way, you can have an interactive virtual exploration of the scene. Think of it like the bullet dodge scene in the first Matrix movie.
As with all emerging technologies, NeRF is not without its flaws. The common problem is that it can overfit training views, causing it to have trouble synthesizing novel views when only a few inputs are available. This is a well known problem known as the few shots neural representation problem.
There have been attempts to address the undershot neural rendering problem. Transfer learning methods and in-depth supervised methods have been tried and successful to some extent. However, these approaches require prior training on large-scale data sets and complex training pipelines, resulting in computational overhead.
What if there was a way to tackle this problem more efficiently? What if we could synthesize novel insights even with little input? time to meet FreeNeRF.
Frequency Regularized NeRF (FreeNeRF) is a novel approach proposed to address the problem of under-firing neural rendering. It’s quite simple to add to a simple NeRF model, as it only requires adding a few lines of code. FreeNeRF introduces two regularization terms: frequency regularization and occlusion regularization.
Rate regularization is used to stabilize the learning process and avoid catastrophic overfitting at the start of training. This is possible thanks to the direct regularization of the visible frequency bands of the NeRF inputs. The observation here is that there is a significant drop in NeRF performance as higher frequency inputs are presented to the model. FreeNeRF uses a visible frequency spectrum-based regularization in the training time step to avoid excess smoothness and gradually provide high-frequency information to NeRF.
Occlusion regularization, on the other hand, is used to penalize density fields close to the camera. These fields cause something called floats, which are artifacts or errors that occur in the rendered image when objects are not correctly aligned with the underlying 3D model. Occlusion regularization targets to eliminate floaters in the NeRF. These artifacts are caused by the less overlapping regions in the training views, which are difficult to estimate due to the limited information available. To get around this, dense fields near the camera are penalized.
FreeNeRF combines these two regularization methods to propose a simple baseline that outperforms previous state-of-the-art methods on multiple data sets. It adds almost no additional computational cost. On top of that, it has no dependencies or overhead, making it a handy and efficient solution to the neural rendering problem of few shots.
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Ekrem Çetinkaya received his B.Sc. in 2018 and M.Sc. in 2019 from Ozyegin University, Istanbul, Türkiye. She wrote her M.Sc. thesis on denoising images using deep convolutional networks. She is currently pursuing a PhD. She graduated from the University of Klagenfurt, Austria, and working as a researcher in the ATHENA project. Her research interests include deep learning, computer vision, and multimedia networks.
