  The accurate reconstruction of dynamic scenes with neural radiance fields is
significantly dependent on the estimation of camera poses. Widely used
structure-from-motion pipelines encounter difficulties in accurately tracking
the camera trajectory when faced with separate dynamics of the scene content
and the camera movement. To address this challenge, we propose DynaMoN. DynaMoN
utilizes semantic segmentation and generic motion masks to handle dynamic
content for initial camera pose estimation and statics-focused ray sampling for
fast and accurate novel-view synthesis. Our novel iterative learning scheme
switches between training the NeRF and updating the pose parameters for an
improved reconstruction and trajectory estimation quality. The proposed
pipeline shows significant acceleration of the training process. We extensively
evaluate our approach on two real-world dynamic datasets, the TUM RGB-D and the
BONN RGB-D Dynamic dataset. DynaMoN improves over the state-of-the-art both in
terms of reconstruction quality and trajectory accuracy. We plan to make our
code public to enhance research in this area.
