  We introduce a novel approach for rendering static and dynamic 3D neural
signed distance functions (SDF) in real-time. We rely on nested neighborhoods
of zero-level sets of neural SDFs, and mappings between them. This framework
supports animations and achieves real-time performance without the use of
spatial data-structures. It consists of three uncoupled algorithms representing
the rendering steps. The multiscale sphere tracing focuses on minimizing
iteration time by using coarse approximations on earlier iterations. The neural
normal mapping transfers details from a fine neural SDF to a surface nested on
a neighborhood of its zero-level set. It is smooth and it does not depend on
surface parametrizations. As a result, it can be used to fetch smooth normals
for discrete surfaces such as meshes and to skip later iterations when sphere
tracing level sets. Finally, we propose an algorithm for analytic normal
calculation for MLPs and describe ways to obtain sequences of neural SDFs to
use with the algorithms.
