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.

通过使用双目输入，引进左右一致性损失函数，并提出一种保存边缘特征的损失函数来改善测试时训练中的深度模糊问题，提出了一种稠密的双目视频深度估计方法，调研结果表明该模型精度可靠。