  We aim to improve the Inverted Neural Radiance Fields (iNeRF) algorithm which
defines the image pose estimation problem as a NeRF based iterative linear
optimization. NeRFs are novel neural space representation models that can
synthesize photorealistic novel views of real-world scenes or objects. Our
contributions are as follows: we extend the localization optimization objective
with a depth-based loss function, we introduce a multi-image based loss
function where a sequence of images with known relative poses are used without
increasing the computational complexity, we omit hierarchical sampling during
volumetric rendering, meaning only the coarse model is used for pose
estimation, and we how that by extending the sampling interval convergence can
be achieved even or higher initial pose estimate errors. With the proposed
modifications the convergence speed is significantly improved, and the basin of
convergence is substantially extended.
