Less is More: Consistent Video Depth Estimation with Masked Frames Modeling. (arXiv:2208.00380v2 [cs.CV] UPDATED)

Temporal consistency is the key challenge of video depth estimation. Previous
works are based on additional optical flow or camera poses, which is
time-consuming. By contrast, we derive consistency with less information. Since
videos inherently exist with heavy temporal redundancy, a missing frame could
be recovered from neighboring ones. Inspired by this, we propose the frame
masking network (FMNet), a spatial-temporal transformer network predicting the
depth of masked frames based on their neighboring frames. By reconstructing
masked temporal features, the …

arxiv consistent cv modeling video