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具有时间一致性的深度。部分方法通过测试时利用几何与重投影约束微调单图像深度模型来实现时间一致性，但这种方式效率低下且不够鲁棒。另一种替代方案是从数据层面学习如何施加时间一致性，但这需要精心设计的模型和充足的视频深度数据。针对这些挑战，我们提出了一种即插即用的框架——神经视频深度稳定器（NVDS），该框架能够稳定不一致的深度估计结果，并可轻松应用于不同单图像深度模型而无需额外操作。此外，我们引入了一个大规模数据集——野外视频深度（VDW），包含14,203个视频片段及超过两百万帧图像，据我们所知，这是目前最大的自然场景视频深度数据集。我们在VDW数据集以及两项公开基准上评估了所提方法，相较以往方法在一致性、准确性和效率方面均展现出显著提升。本研究可作为坚实的基线，并为基于学习的视频深度模型提供数据基础。我们将在未来研究中公开数据集与代码。