Neural Radiance Fields has become a prominent method of scene generation via view synthesis. A critical requirement for the original algorithm to learn meaningful scene representation is camera pose information for each image in a data set. Current approaches try to circumnavigate this assumption with moderate success, by learning approximate camera positions alongside learning neural representations of a scene. This requires complicated camera models, causing a long and complicated training process, or results in a lack of texture and sharp details in rendered scenes. In this work we introduce Hash Color Correction (HashCC) -- a lightweight method for improving Neural Radiance Fields rendered image quality, applicable also in situations where camera positions for a given set of images are unknown.

翻译：摘要：神经辐射场已成为通过视图合成进行场景生成的主流方法。原始算法学习有意义的场景表示的关键要求是数据集中每张图像的相机位姿信息。当前方法试图通过在学习场景神经表示的同时学习近似相机位置来规避这一假设，但效果有限。这需要复杂的相机模型，导致训练过程漫长且复杂，或者导致渲染场景缺乏纹理和清晰细节。在本文中，我们提出哈希颜色校正（HashCC）——一种轻量级方法，用于提升神经辐射场渲染图像质量，该方法同样适用于给定图像集相机位置未知的情况。