HashCC: Lightweight Method to Improve the Quality of the Camera-less
  NeRF Scene Generation
Jan Olszewski
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.