Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth (2203.15174v2)

Abstract: Conventional self-supervised monocular depth prediction methods are based on a static environment assumption, which leads to accuracy degradation in dynamic scenes due to the mismatch and occlusion problems introduced by object motions. Existing dynamic-object-focused methods only partially solved the mismatch problem at the training loss level. In this paper, we accordingly propose a novel multi-frame monocular depth prediction method to solve these problems at both the prediction and supervision loss levels. Our method, called DynamicDepth, is a new framework trained via a self-supervised cycle consistent learning scheme. A Dynamic Object Motion Disentanglement (DOMD) module is proposed to disentangle object motions to solve the mismatch problem. Moreover, novel occlusion-aware Cost Volume and Re-projection Loss are designed to alleviate the occlusion effects of object motions. Extensive analyses and experiments on the Cityscapes and KITTI datasets show that our method significantly outperforms the state-of-the-art monocular depth prediction methods, especially in the areas of dynamic objects. Code is available at https://github.com/AutoAILab/DynamicDepth

• The paper introduces DynamicDepth, featuring a novel module that decouples object motion to replace mismatched image patches with accurate projections.
• The paper designs an occlusion-aware cost volume and re-projection loss that manage occluded regions for improved depth prediction across temporal frames.
• The paper validates its approach on Cityscapes and KITTI datasets, demonstrating significant performance gains in dynamic scenes for monocular depth estimation.

Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth

The paper "Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth" addresses the complexities involved in predicting monocular depth in dynamic scenes using multi-frame input. The authors identify two primary issues with conventional self-supervised methods: object motion causing mismatches and occlusion problems, which traditional approaches tend to inadequately handle. Their proposed solution, DynamicDepth, innovates at both the prediction and supervision loss levels and incorporates several novel components to better capture and predict depth information in the presence of dynamic objects.

Key Innovations and Methodologies

1. Dynamic Object Motion Disentanglement (DOMD): A central feature of this framework is DOMD, a module designed to decouple motion dynamics in the scene, addressing the mismatch problem by leveraging depth priors. It enables the replacement of dynamically mismatched image patches with more accurately projected ones, effectively improving the cost volume construction and re-projection loss function.
2. Occlusion-aware Cost Volume: The method constructs a cost volume that accounts for occlusion arising from object motion, thereby enhancing geometric reasoning across temporal frames. This approach mitigates the contamination of cost distribution caused by occluded pixel comparisons.
3. Occlusion-aware Re-projection Loss: The authors propose a re-projection loss mechanism that actively handles occlusions by switching reference frames to ensure non-occluded source pixels are used for projecting photometric consistency, thus improving training paradigms.
4. Dynamic Object Cycle Consistency: By implementing this novel consistency loss, DynamicDepth allows mutual reinforcement between initial depth priors and final depth predictions. This integration produces more robust and accurate predictions by iterating improvements between the initially predicted and dynamically adjusted depths.

Experimental Validation

The paper presents an extensive evaluation against state-of-the-art depth prediction methods, highlighting the efficacy of DynamicDepth on prominent datasets such as Cityscapes and KITTI. DynamicDepth consistently outperforms previous techniques, particularly in dynamic scenarios where conventional methods struggle. On the Cityscapes dataset, which contains a significant proportion of dynamic objects, DynamicDepth demonstrates a substantial performance gain, thereby validating its approach to solving the challenges posed by motion and occlusion.

Moreover, the quantitative metrics provided on the KITTI dataset show the proposed method's robustness, even when the proportion of dynamic objects is lesser compared to datasets like Cityscapes. The results on improved KITTI ground truth further assert that DynamicDepth's capability extends beyond conventional challenging frames.

Theoretical and Practical Implications

From a theoretical perspective, DynamicDepth provides a novel framework that jointly reasons spatial and temporal dynamics without explicit object motion prediction, offering a more integrated solution to multi-frame depth prediction challenges. This approach can inspire further research into bridging unsupervised monocular depth prediction with real-world dynamic scenes, thus pushing forward the boundaries of self-supervised learning frameworks.

Practically, DynamicDepth's methodologies could significantly enhance applications in autonomous navigation, augmented reality, and robotics—areas where precise depth prediction is paramount yet traditionally limited by dynamic interference. Its implementation has the potential to reduce the complexity and overhead associated with sensor-based approaches, offering a more accessible and scalable solution for real-time applications.

Future Directions

While this paper addresses critical facets of dynamic object handling in depth prediction, future research could investigate more generalized forms of motion beyond short temporal windows, incorporate additional environmental dynamics, and leverage advanced segmentation techniques to refine motion disambiguation further. Moreover, exploring the integration of this framework with other forms of sensory input, such as inertial data, could yield improvements in holistic scene understanding and context-aware systems.

In conclusion, "Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth" offers a comprehensive solution to dynamic depth prediction challenges, presenting significant advancements in both theoretical understanding and practical applications, thus contributing valuable insights to the field of computer vision and AI.