Neural Radiance Fields with Regularizer Based on Differences of Neighboring Pixels

Neural Radiance Fields (NeRF) have significantly impacted the novel view synthesis task. While it can render realistic images from unseen viewpoints, the boundaries of the object or scene in the areas rapidly changing the color and density are blurred. We present a novel NeRF regularization based on neighboring pixels’ differences to resolve this issue. This proposed regularized NeRF is named Neighboring Pixels Differences Regularized Neural Radiance Fields. Our key insight is that the pixel neighborhood relation in the image has essential information about the structure of the object and scene. Therefore, we propose to incorporate the neighborhood information into the original NeRF. We can easily implement the proposed method by adding a regularization term based on pixel neighborhood relation into the loss function of the vanilla NeRF. In the experiments, to evaluate the effectiveness of our method, we compared the proposed method with the original NeRF using synthetic and real-world multiview data. The experimental results showed that our method allowed for rendering high-quality novel views for both settings than the original NeRF.