  We propose Neural-DynamicReconstruction (NDR), a template-free method to
recover high-fidelity geometry and motions of a dynamic scene from a monocular
RGB-D camera. In NDR, we adopt the neural implicit function for surface
representation and rendering such that the captured color and depth can be
fully utilized to jointly optimize the surface and deformations. To represent
and constrain the non-rigid deformations, we propose a novel neural invertible
deforming network such that the cycle consistency between arbitrary two frames
is automatically satisfied. Considering that the surface topology of dynamic
scene might change over time, we employ a topology-aware strategy to construct
the topology-variant correspondence for the fused frames. NDR also further
refines the camera poses in a global optimization manner. Experiments on public
datasets and our collected dataset demonstrate that NDR outperforms existing
monocular dynamic reconstruction methods.
