  Novel view synthesis (NVS) is a challenging task in computer vision that
involves synthesizing new views of a scene from a limited set of input images.
Neural Radiance Fields (NeRF) have emerged as a powerful approach to address
this problem, but they require accurate knowledge of camera \textit{intrinsic}
and \textit{extrinsic} parameters. Traditionally, structure-from-motion (SfM)
and multi-view stereo (MVS) approaches have been used to extract camera
parameters, but these methods can be unreliable and may fail in certain cases.
In this paper, we propose a novel technique that leverages unposed images from
dynamic datasets, such as the NVIDIA dynamic scenes dataset, to learn camera
parameters directly from data. Our approach is highly extensible and can be
integrated into existing NeRF architectures with minimal modifications. We
demonstrate the effectiveness of our method on a variety of static and dynamic
scenes and show that it outperforms traditional SfM and MVS approaches. The
code for our method is publicly available at
\href{https://github.com/redacted/refinerf}{https://github.com/redacted/refinerf}.
Our approach offers a promising new direction for improving the accuracy and
robustness of NVS using NeRF, and we anticipate that it will be a valuable tool
for a wide range of applications in computer vision and graphics.
