  Video depth estimation is crucial in various applications, such as scene
reconstruction and augmented reality. In contrast to the naive method of
estimating depths from images, a more sophisticated approach uses temporal
information, thereby eliminating flickering and geometrical inconsistencies. We
propose a consistent method for dense video depth estimation; however, unlike
the existing monocular methods, ours relates to stereo videos. This technique
overcomes the limitations arising from the monocular input. As a benefit of
using stereo inputs, a left-right consistency loss is introduced to improve the
performance. Besides, we use SLAM-based camera pose estimation in the process.
To address the problem of depth blurriness during test-time training (TTT), we
present an edge-preserving loss function that improves the visibility of fine
details while preserving geometrical consistency. We show that our edge-aware
stereo video model can accurately estimate the dense depth maps.
