  We present an explicit-grid based method for efficiently reconstructing
streaming radiance fields for novel view synthesis of real world dynamic
scenes. Instead of training a single model that combines all the frames, we
formulate the dynamic modeling problem with an incremental learning paradigm in
which per-frame model difference is trained to complement the adaption of a
base model on the current frame. By exploiting the simple yet effective tuning
strategy with narrow bands, the proposed method realizes a feasible framework
for handling video sequences on-the-fly with high training efficiency. The
storage overhead induced by using explicit grid representations can be
significantly reduced through the use of model difference based compression. We
also introduce an efficient strategy to further accelerate model optimization
for each frame. Experiments on challenging video sequences demonstrate that our
approach is capable of achieving a training speed of 15 seconds per-frame with
competitive rendering quality, which attains $1000 \times$ speedup over the
state-of-the-art implicit methods. Code is available at
https://github.com/AlgoHunt/StreamRF.
