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

TL;DR

HashCC is a lightweight technique that enhances the quality of camera-less Neural Radiance Fields scene generation, addressing issues of texture and detail loss without requiring camera pose data.

Contribution

The paper introduces Hash Color Correction (HashCC), a novel method that improves NeRF rendering quality in camera-less scenarios with a simpler, more efficient approach.

Findings

HashCC improves scene rendering quality without camera pose data.

The method enhances texture and sharpness in NeRF outputs.

HashCC reduces training complexity compared to existing approaches.

Abstract

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.