  For robotics applications where there is a limited number of (typically
ego-centric) views, parametric representations such as neural radiance fields
(NeRFs) generalize better than non-parametric ones such as Gaussian splatting
(GS) to views that are very different from those in the training data; GS
however can render much faster than NeRFs. We develop a procedure to convert
back and forth between the two. Our approach achieves the best of both NeRFs
(superior PSNR, SSIM, and LPIPS on dissimilar views, and a compact
representation) and GS (real-time rendering and ability for easily modifying
the representation); the computational cost of these conversions is minor
compared to training the two from scratch.
