  Implicit representations are widely used for object reconstruction due to
their efficiency and flexibility. In 2021, a novel structure named neural
implicit map has been invented for incremental reconstruction. A neural
implicit map alleviates the problem of inefficient memory cost of previous
online 3D dense reconstruction while producing better quality. % However, the
neural implicit map suffers the limitation that it does not support remapping
as the frames of scans are encoded into a deep prior after generating the
neural implicit map. This means, that neither this generation process is
invertible, nor a deep prior is transformable. The non-remappable property
makes it not possible to apply loop-closure techniques. % We present a neural
implicit map based transformation algorithm to fill this gap. As our neural
implicit map is transformable, our model supports remapping for this special
map of latent features. % Experiments show that our remapping module is capable
to well-transform neural implicit maps to new poses. Embedded into a SLAM
framework, our mapping model is able to tackle the remapping of loop closures
and demonstrates high-quality surface reconstruction. % Our implementation is
available at github\footnote{\url{https://github.com/Jarrome/IMT_Mapping}} for
the research community.
