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

Understand

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.