While the use of neural radiance fields (NeRFs) in different challenging settings has been explored, only very recently have there been any contributions that focus on the use of NeRF in foggy environments. We argue that the traditional NeRF models are able to replicate scenes filled with fog and propose a method to remove the fog when synthesizing novel views. By calculating the global contrast of a scene, we can estimate a density threshold that, when applied, removes all visible fog. This makes it possible to use NeRF as a way of rendering clear views of objects of interest located in fog-filled environments. Additionally, to benchmark performance on such scenes, we introduce a new dataset that expands some of the original synthetic NeRF scenes through the addition of fog and natural environments. The code, dataset, and video results can be found on our project page: https://vegardskui.com/fognerf/

翻译：尽管神经辐射场（NeRF）在不同具有挑战性的场景中的应用已有探索，但直到最近才出现聚焦于NeRF在雾霾环境中使用的研究贡献。我们认为传统NeRF模型能够重现充满雾霾的场景，并提出一种在合成新视角时去除雾霾的方法。通过计算场景的全局对比度，我们可以估计一个密度阈值，应用该阈值即可去除所有可见雾霾。这使得NeRF能够用于渲染雾霾环境中目标物体的清晰视图。此外，为了对此类场景的性能进行基准测试，我们引入了一个新数据集，该数据集通过添加雾霾和自然环境扩展了部分原始合成NeRF场景。代码、数据集和视频结果可在我们的项目页面中找到：https://vegardskui.com/fognerf/