Video Depth Propagation

Understand

Depth estimation in videos is essential for visual perception in real-world applications.

• However, existing methods either rely on simple frame-by-frame monocular models, leading to temporal inconsistencies and inaccuracies, or use computationally demanding temporal modeling, unsuitable for real-time applications.
• These limitations significantly restrict general applicability and performance in practical settings.
• To address this, we propose VeloDepth, an efficient and robust online video depth estimation pipeline that effectively leverages spatiotemporal priors from previous depth predictions and performs deep feature propagation.