IFFNeRF: Initialisation Free and Fast 6DoF pose estimation from a single
  image and a NeRF model
Matteo Bortolon Theodore Tsesmelis Stuart James Fabio Poiesi Alessio Del Bue
We introduce IFFNeRF to estimate the six degrees-of-freedom (6DoF) camera
pose of a given image, building on the Neural Radiance Fields (NeRF)
formulation. IFFNeRF is specifically designed to operate in real-time and
eliminates the need for an initial pose guess that is proximate to the sought
solution. IFFNeRF utilizes the Metropolis-Hasting algorithm to sample surface
points from within the NeRF model. From these sampled points, we cast rays and
deduce the color for each ray through pixel-level view synthesis. The camera
pose can then be estimated as the solution to a Least Squares problem by
selecting correspondences between the query image and the resulting bundle. We
facilitate this process through a learned attention mechanism, bridging the
query image embedding with the embedding of parameterized rays, thereby
matching rays pertinent to the image. Through synthetic and real evaluation
settings, we show that our method can improve the angular and translation error
accuracy by 80.1% and 67.3%, respectively, compared to iNeRF while performing
at 34fps on consumer hardware and not requiring the initial pose guess.