Zero-Shot Monocular Scene Flow Estimation in the Wild

Abstract: Large models have shown generalization across datasets for many low-level vision tasks, like depth estimation, but no such general models exist for scene flow. Even though scene flow has wide potential use, it is not used in practice because current predictive models do not generalize well. We identify three key challenges and propose solutions for each. First, we create a method that jointly estimates geometry and motion for accurate prediction. Second, we alleviate scene flow data scarcity with a data recipe that affords us 1M annotated training samples across diverse synthetic scenes. Third, we evaluate different parameterizations for scene flow prediction and adopt a natural and effective parameterization. Our resulting model outperforms existing methods as well as baselines built on large-scale models in terms of 3D end-point error, and shows zero-shot generalization to the casually captured videos from DAVIS and the robotic manipulation scenes from RoboTAP. Overall, our approach makes scene flow prediction more practical in-the-wild.

• The paper introduces a joint geometry-motion framework that minimizes error propagation between depth and motion estimates.
• It leverages a large-scale synthetic dataset of one million examples to enhance model generalization across diverse real-world environments.
• The model achieves lower 3D end-point error and robust zero-shot performance on benchmarks like DAVIS and RoboTAP compared to existing methods.

Zero-Shot Monocular Scene Flow Estimation in the Wild

This paper addresses the challenge of monocular scene flow (SF) estimation, particularly emphasizing its application in varied, real-world environments, often referred to as “the wild.” Scene flow estimation, which involves predicting the 3D motion of objects within a scene, has not traditionally been widely adopted in practical settings due to its limited generalization capabilities. The authors propose a novel approach that enables zero-shot monocular scene flow estimation, aiming to bridge this significant gap in applicability.

Key Contributions

1. Joint Geometry and Motion Estimation: The approach integrates the calculation of both geometry and motion within the same framework. The entanglement of depth and motion in image space necessitates their concurrent estimation to avoid the propagation of errors from one domain to the other.
2. Large-Scale, Diverse Training Dataset: The method addresses the typical issue of data scarcity by creating a substantial dataset, consisting of 1 million annotated examples from diverse synthetic scenes. This comprehensive dataset is crucial for training models that need to generalize well across different real-world situations.
3. Effective Parameterization and Scale-Alignment: The model evaluates various parameterizations for scene flow prediction, adopting a natural and effective approach. Importantly, it includes a scale-alignment mechanism to exploit both metric and relative datasets, which is critical given the diversity in scales among different datasets.

Strong Numerical Results

The proposed method demonstrates superior performance across several metrics compared to existing scene flow and baseline models built on large-scale frameworks. The model achieves remarkable gains in 3D end-point error and exhibits strong generalization to datasets not seen during training. Specifically, it shows zero-shot generalization on real-world datasets such as DAVIS and RoboTAP, outperforming established methods like Self-Mono-SF and OpticalExpansion.

Theoretical and Practical Implications

From a theoretical perspective, the development of a zero-shot, generalizable scene flow model enhances the understanding of joint geometry-motion estimation and highlights the importance of robust training datasets. Practically, the proposed model broadens the scope for scene flow application across various fields such as autonomous driving, augmented reality, and robotics, where dynamic scene understanding is crucial. By overcoming data disparity and scale challenges, it sets a precedent for future developments in deploying scene flow estimation models in real-world applications without extensive retraining.

Future Directions

The study opens avenues for further research in enhancing the robustness and accuracy of scene flow models, especially in complex scenarios involving multiple moving objects and varying lighting conditions. Additionally, exploring the integration of this approach with other low-level vision tasks could unveil new possibilities for unified models capable of performing multiple tasks simultaneously. Future exploration could also involve refining the scale-alignment techniques to unpack even more accurate scene representations from diverse data sources.

In summary, this paper marks a significant step towards practical, real-world applicability of monocular scene flow estimation, providing a foundation for further developments in the field. The proposed method not only addresses current limitations but also paves the way for new technological advancements and applications.