HashCC: Lightweight Method to Improve the Quality of the Camera-less NeRF Scene Generation
This addresses the challenge of scene generation for applications where camera pose data is unavailable, offering a lightweight solution to enhance texture and detail, though it appears incremental as it builds on existing camera-less NeRF methods.
The paper tackles the problem of generating high-quality Neural Radiance Fields (NeRF) scenes without known camera poses, introducing Hash Color Correction (HashCC) to improve rendered image quality in such camera-less settings.
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.