  Temporally consistent depth estimation is crucial for online applications
such as augmented reality. While stereo depth estimation has received
substantial attention as a promising way to generate 3D information, there is
relatively little work focused on maintaining temporal stability. Indeed, based
on our analysis, current techniques still suffer from poor temporal
consistency. Stabilizing depth temporally in dynamic scenes is challenging due
to concurrent object and camera motion. In an online setting, this process is
further aggravated because only past frames are available. We present a
framework named Consistent Online Dynamic Depth (CODD) to produce temporally
consistent depth estimates in dynamic scenes in an online setting. CODD
augments per-frame stereo networks with novel motion and fusion networks. The
motion network accounts for dynamics by predicting a per-pixel SE3
transformation and aligning the observations. The fusion network improves
temporal depth consistency by aggregating the current and past estimates. We
conduct extensive experiments and demonstrate quantitatively and qualitatively
that CODD outperforms competing methods in terms of temporal consistency and
performs on par in terms of per-frame accuracy.
