ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving Cameras in the Wild (2207.09137v1)

Abstract: Estimating the pose of a moving camera from monocular video is a challenging problem, especially due to the presence of moving objects in dynamic environments, where the performance of existing camera pose estimation methods are susceptible to pixels that are not geometrically consistent. To tackle this challenge, we present a robust dense indirect structure-from-motion method for videos that is based on dense correspondence initialized from pairwise optical flow. Our key idea is to optimize long-range video correspondence as dense point trajectories and use it to learn robust estimation of motion segmentation. A novel neural network architecture is proposed for processing irregular point trajectory data. Camera poses are then estimated and optimized with global bundle adjustment over the portion of long-range point trajectories that are classified as static. Experiments on MPI Sintel dataset show that our system produces significantly more accurate camera trajectories compared to existing state-of-the-art methods. In addition, our method is able to retain reasonable accuracy of camera poses on fully static scenes, which consistently outperforms strong state-of-the-art dense correspondence based methods with end-to-end deep learning, demonstrating the potential of dense indirect methods based on optical flow and point trajectories. As the point trajectory representation is general, we further present results and comparisons on in-the-wild monocular videos with complex motion of dynamic objects. Code is available at https://github.com/bytedance/particle-sfm.

• The paper presents a novel dense indirect SfM method that uses long-range video correspondences to enhance camera pose estimation in dynamic environments.
• It employs a specialized network to segment static from dynamic trajectories, achieving substantial reductions in Absolute Trajectory Error on benchmarks like MPI Sintel.
• The approach demonstrates practical potential for augmented reality and robotics by robustly localizing moving cameras amidst challenging, dynamic scenes.

Exploiting Dense Point Trajectories for Camera Localization in Dynamic Environments

The paper "ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving Cameras in the Wild" presents an innovative approach within the field of Structure-from-Motion (SfM), leveraging dense point trajectories for the localization of moving cameras in dynamic environments. The methodology enhances the accuracy and robustness of camera pose estimation from traditional monocular video inputs, tackling the significant challenge imposed by dynamic objects within the scene.

Methodological Contributions

The paper introduces a dense indirect SfM framework that capitalizes on dense video correspondences initially derived from pairwise optical flow. This foundation enables the construction of long-range video correspondences represented as dense point trajectories. A notable contribution is a novel network architecture designed for processing irregular point trajectory data, allowing robust motion segmentation and classification between dynamic and static components within the scene.

The incorporation of this trajectory-based approach facilitates a refined global bundle adjustment process. By focusing on static trajectories, the system optimizes camera poses efficiently, thereby circumventing traditional limitations encountered in highly dynamic scenes. Furthermore, the segmentation performance surpasses existing state-of-the-art methods, underscoring the efficacy of the proposed trajectory processing network in discerning dynamic from static regions.

Experimental Validation and Comparative Analysis

The experimental evaluation on challenging datasets, including MPI Sintel and ScanNet, illuminates the strengths of the proposed approach. On the Sintel dataset, the method achieves a substantial reduction in Absolute Trajectory Error (ATE) compared to baselines, including leading methods such as COLMAP. The results reveal the capacity of the method to produce significant improvements in localization accuracy while maintaining robustness against movement-rich environments. On the ScanNet dataset, although the approach does not incorporate loop closure, it demonstrates competitive relative pose error performance, highlighting its potential in various scenarios.

Theoretical and Practical Implications

Theoretically, this paper challenges the classical paradigms of visual SLAM and SfM that predominantly rely on sparse feature points. By leveraging dense point trajectories, the paper opens avenues for SfM systems that can adaptively handle dynamic scenes without heavy reliance on semantic segmentation of potentially moving objects. Practically, the implications of this research are profound, suggesting the potential for real-world applications in areas like augmented reality and robotics where robust camera localization in dynamic environments is crucial.

Future Directions

Looking forward, several enhancements can be envisaged. Integration of loop closure mechanisms could bolster long-term accuracy in monotonous landscapes like indoor environments. Moreover, the combination with depth estimation techniques may further enhance the trajectory processing network's discernment of three-dimensional motion patterns, potentially improving localization accuracy across more varied datasets.

In summary, the research offers a novel perspective on dense indirect approaches in SfM, underscoring the potential of dense point trajectories to advance robustness and accuracy in camera localization across dynamic and challenging environments. The approach stands as a foundation for further exploration and development of SfM systems capable of high-fidelity visual reconstruction in real-world scenarios.