  Video depth estimation aims to infer temporally consistent depth. Some
methods achieve temporal consistency by finetuning a single-image depth model
during test time using geometry and re-projection constraints, which is
inefficient and not robust. An alternative approach is to learn how to enforce
temporal consistency from data, but this requires well-designed models and
sufficient video depth data. To address these challenges, we propose a
plug-and-play framework called Neural Video Depth Stabilizer (NVDS) that
stabilizes inconsistent depth estimations and can be applied to different
single-image depth models without extra effort. We also introduce a large-scale
dataset, Video Depth in the Wild (VDW), which consists of 14,203 videos with
over two million frames, making it the largest natural-scene video depth
dataset to our knowledge. We evaluate our method on the VDW dataset as well as
two public benchmarks and demonstrate significant improvements in consistency,
accuracy, and efficiency compared to previous approaches. Our work serves as a
solid baseline and provides a data foundation for learning-based video depth
models. We will release our dataset and code for future research.
