  The neural radiance field (NeRF) for realistic novel view synthesis requires
camera poses to be pre-acquired by a structure-from-motion (SfM) approach. This
two-stage strategy is not convenient to use and degrades the performance
because the error in the pose extraction can propagate to the view synthesis.
We integrate the pose extraction and view synthesis into a single end-to-end
procedure so they can benefit from each other. For training NeRF models, only
RGB images are given, without pre-known camera poses. The camera poses are
obtained by the epipolar constraint in which the identical feature in different
views has the same world coordinates transformed from the local camera
coordinates according to the extracted poses. The epipolar constraint is
jointly optimized with pixel color constraint. The poses are represented by a
CNN-based deep network, whose input is the related frames. This joint
optimization enables NeRF to be aware of the scene's structure that has an
improved generalization performance. Extensive experiments on a variety of
scenes demonstrate the effectiveness of the proposed approach. Code is
available at https://github.com/XTU-PR-LAB/SaNerf.
