POMATO: Marrying Pointmap Matching with Temporal Motion for Dynamic 3D Reconstruction (2504.05692v1)

Abstract: 3D reconstruction in dynamic scenes primarily relies on the combination of geometry estimation and matching modules where the latter task is pivotal for distinguishing dynamic regions which can help to mitigate the interference introduced by camera and object motion. Furthermore, the matching module explicitly models object motion, enabling the tracking of specific targets and advancing motion understanding in complex scenarios. Recently, the proposed representation of pointmap in DUSt3R suggests a potential solution to unify both geometry estimation and matching in 3D space, but it still struggles with ambiguous matching in dynamic regions, which may hamper further improvement. In this work, we present POMATO, a unified framework for dynamic 3D reconstruction by marrying pointmap matching with temporal motion. Specifically, our method first learns an explicit matching relationship by mapping RGB pixels from both dynamic and static regions across different views to 3D pointmaps within a unified coordinate system. Furthermore, we introduce a temporal motion module for dynamic motions that ensures scale consistency across different frames and enhances performance in tasks requiring both precise geometry and reliable matching, most notably 3D point tracking. We show the effectiveness of the proposed pointmap matching and temporal fusion paradigm by demonstrating the remarkable performance across multiple downstream tasks, including video depth estimation, 3D point tracking, and pose estimation. Code and models are publicly available at https://github.com/wyddmw/POMATO.

Dynamic 3D Reconstruction Using POMATO: Technical Insights

The paper "POMATO: Marrying Pointmap Matching with Temporal Motions for Dynamic 3D Reconstruction" presents a novel framework aimed at tackling the challenges associated with dynamic 3D reconstruction, where moving objects and camera dynamics pose significant hurdles. POMATO builds on previous methodologies like DUSt3R but surpasses them by integrating explicit pointmap matching with temporal motion estimation in the 3D space.

Technical Contributions

• Pointmap Matching: The essence of the proposed framework lies in its ability to explicitly handle dynamic regions by mapping RGB pixels across different views into a unified 3D coordinate system. This technique contrasts with DUSt3R's reliance on rigid transformations that falter in dynamic scenes, leading to ambiguous 3D matching. POMATO introduces a pointmap matching head that conditions the prediction on features from the first view, establishing a precise correspondence essential for motion analysis and understanding.
• Temporal Motion Module: Extending beyond pairwise image input, POMATO incorporates a temporal motion module designed to enhance interactions along the temporal dimension, thereby improving scale consistency across frames. This module is particularly impactful in applications requiring precise estimation and tracking over video frames, such as 3D point tracking.
• Unified Framework for Motion Estimation and Geometry: POMATO unifies geometry estimation and object motion understanding in a single framework, which is critical for dynamic scene reconstruction. By leveraging temporal motion and pointmap matching, the framework substantially improves upon previous methods that struggled with domain gaps and accumulated errors due to reliance on separate optical flow modules.

Numerical Results and Implications

The paper demonstrates the potential of POMATO across various tasks, including video depth estimation, 3D point tracking, and pose estimation. Importantly, the method achieves competitive performance in scale-invariant depth estimation, surpassing CUT3R in terms of online inference speed while maintaining high precision. In 3D tracking evaluation, POMATO exhibits improvements in APD metrics, highlighting its robustness even compared to specialized models such as SpatialTracker that benefit from ground-truth camera intrinsics.

Implications for AI and Future Work

The practical implications extend to various fields relying on dynamic scene reconstruction, such as SLAM, robotics, and autonomous driving. The proposed framework's ability to maintain consistency and accuracy without auxiliary modules paves the way for real-time applications in environments with unpredictable object movement. Future work may involve scaling up training with additional matching datasets, improved representation learning for dynamic conditions, and exploring the integration with larger temporal windows for more extensive sequential inputs.

In summary, the paper introduces a substantial advancement in dynamic 3D reconstruction by combining pointmap matching with temporal motion analysis in a unified framework, establishing a strong foundation for future research and applications in the field. The presented method not only progresses the theoretical understanding of dynamic matching but also sets a new standard for practical implementations across real-world scenarios.