Abstract-Level Summary

The paper introduces Video Depth Anything, an innovative method aimed at addressing temporal inconsistencies in monocular depth estimation for long videos. The proposed model builds upon Depth Anything V2, integrating a novel spatial-temporal head and a temporal consistency loss based on depth gradients, which eliminates the need for geometric priors. Tested on various benchmarks, the model excels in zero-shot video depth estimation, supporting extended video lengths without compromising on accuracy or efficiency, and achieves state-of-the-art results in both spatial and temporal performance.

Introduction Highlights

The study identifies a significant limitation in existing monocular depth estimation models, particularly their temporal inconsistencies in video applications, affecting areas such as robotics and augmented reality. The objective is to develop a method that inherits the advantages of existing models while ensuring temporal stability across long videos. The research posits that a new model can achieve this without relying on geometric or generative priors, addressing the pressing need for temporally consistent depth estimation in extended video sequences.

Methodology

The researchers designed a hybrid model by augmenting Depth Anything V2 with a lightweight spatial-temporal head and incorporating temporal consistency features. This was accomplished through a temporal gradient matching loss that enhances temporal depth prediction, applied without optical flow dependencies. The model was trained on a mixed dataset of 550K labeled video frames and 0.62 million unlabeled images using both supervised and self-training approaches. A novel segment-wise inference strategy was also developed to ensure efficient depth estimation for super-long videos.

Key Findings

• The model outperformed existing benchmarks in terms of temporal consistency and spatial accuracy for long video sequences.
• Achieved top-tier performance on zero-shot video depth estimation across various datasets, including KITTI and Scannet.
• The method demonstrated capability for real-time video depth estimation at 30 FPS with smaller model versions, comparable to advanced diffusion-based methods but with enhanced efficiency.

Implications and Contributions

This study pushes forward the technology of monocular depth estimation by resolving temporal inconsistencies over long video durations. It provides practical benefits for industries requiring stable long-duration video processing, such as augmented reality and advanced video editing. The introduction of a non-reliant method on optical flow simplifies the process and broadens potential application scopes, representing a significant advancement in the field.

Conclusion

The new model notably advances both the temporal stability and efficiency in monocular video depth estimation, addressing the temporal inconsistency limitations of previous models. While results are promising, the study suggests further exploration into enhancing performance with larger datasets and streamlining efficiency for continuous video streams.

Glossary

1. Monocular Depth Estimation (MDE): The process of determining the depth of objects from single-camera images or video sequences.
2. Temporal Consistency: The property ensuring that depth estimations remain stable over consecutive video frames, reducing flickering and inconsistencies.
3. Spatial-Temporal Head: A computational module designed to process spatial and temporal information from video frames to improve depth estimation accuracy.
4. Optical Flow: A technique used to estimate the motion of objects between successive frames, historically used to improve temporal consistency.
5. Zero-shot Learning: The ability of a model to accurately make predictions on data it has not been explicitly trained on.
6. Temporal Gradient Matching Loss: A loss function designed to ensure consistency by aligning the gradients of predicted depths with those observed in reality.
7. Inference Strategy: The method or approach used to apply a trained model to new data for making predictions, particularly for long video sequences.