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

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