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.

翻译：视频深度估计旨在推断时间上一致的深度。一些方法通过测试时利用几何和重投影约束微调单幅图像深度模型来实现时间一致性，但这种方法效率低且不稳健。另一种方法是从数据中学习如何强制时间一致性，但这需要精心设计的模型和足够的视频深度数据。为解决这些挑战，我们提出了一种即插即用框架——神经视频深度稳定器（NVDS），它能稳定不一致的深度估计，并可无需额外工作地应用于不同的单幅图像深度模型。我们还引入了一个大规模数据集——野外视频深度（VDW），该数据集包含14,203个视频，超过两百万帧，据我们所知，这是最大的自然场景视频深度数据集。我们在VDW数据集以及两个公开基准上评估了我们的方法，并证明了与先前方法相比，在一致性、准确性和效率方面有显著提升。我们的工作为基于学习的视频深度模型提供了坚实的基线及数据基础。我们将公开数据集和代码以供未来研究。