ColabSfM: Collaborative Structure-from-Motion by Point Cloud Registration (2503.17093v1)

Abstract: Structure-from-Motion (SfM) is the task of estimating 3D structure and camera poses from images. We define Collaborative SfM (ColabSfM) as sharing distributed SfM reconstructions. Sharing maps requires estimating a joint reference frame, which is typically referred to as registration. However, there is a lack of scalable methods and training datasets for registering SfM reconstructions. In this paper, we tackle this challenge by proposing the scalable task of point cloud registration for SfM reconstructions. We find that current registration methods cannot register SfM point clouds when trained on existing datasets. To this end, we propose a SfM registration dataset generation pipeline, leveraging partial reconstructions from synthetically generated camera trajectories for each scene. Finally, we propose a simple but impactful neural refiner on top of the SotA registration method RoITr that yields significant improvements, which we call RefineRoITr. Our extensive experimental evaluation shows that our proposed pipeline and model enables ColabSfM. Code is available at https://github.com/EricssonResearch/ColabSfM

Insights into Collaborative Structure-from-Motion by Point Cloud Registration

The paper "ColabSfM: Collaborative Structure-from-Motion by Point Cloud Registration" provides a comprehensive exploration of the collaborative structure-from-motion (SfM) paradigm, focusing on merging distributed SfM reconstructions. The authors propose a novel approach to address the registration of SfM point clouds using geometrical information alone, without relying on visual descriptors. This approach entails significant implications for the advancement of scalable mapping and localization technologies, especially in application areas involving robots and extended reality devices.

Key Contributions

1. Point Cloud Registration for SfM: The paper introduces a method for registering SfM point clouds by leveraging the geometric properties of 3D points and their normals. This method circumvents issues related to descriptor compatibility and scalability that arise when using traditional descriptor-based approaches. The authors assert that matching based solely on geometry can be effective, provided a suitable dataset is available for training.
2. Synthetic Dataset Generation: To enable effective training of point cloud registration models, the authors present a scalable pipeline for generating synthetic datasets specifically designed for SfM registration. This pipeline uses synthetic camera trajectories to create partial reconstructions, consequently overcoming limitations found in existing datasets. The training dataset generated comprises diverse scenarios showcasing varying viewpoints and scales, aiding robust model performance across disparate SfM tasks.
3. RefineRoITr Model: Building upon the RoITr model, the paper proposes the RefineRoITr, which includes a refinement stage to enhance the matching precision of point cloud registries. The refinement stage is achieved via a local Transformer that processes fine features and improves the match quality by considering local neighborhoods around coarse matches.

Experimental Evaluation

The paper details extensive experiments over several datasets, including MegaDepth, Cambridge Landmarks, and the challenging Quad6k dataset, showcasing the efficacy of the proposed methods against established baselines such as OverlapPredator and GeoTransformer. Results demonstrate the superior performance and generalization capabilities of models trained on the proposed synthetic dataset across typical SfM scenarios, including varied overlap and scale conditions.

Implications

The implications of this research are manifold:

• Scalability: By eliminating the dependency on visual descriptors, the proposed approach significantly reduces the data storage requirements and computational overhead involved in merging SfM maps.
• Privacy: The descriptor-free nature guards against potential privacy breaches posed by visual descriptor inversion attacks.
• Interoperability: The approach paves the way for improved interoperability among devices from different vendors, a critical need for industry applications such as autonomous robotics and XR technologies.

Future Directions

The paper opens up several avenues for future research, particularly in addressing:

• Optimization techniques to further reduce computational demands during the registration process.
• Enhancements to the model's ability to deal with symmetric scenes, which may pose challenges due to inherent geometrical properties.
• Exploration of generalization across different types of detectors, extending the robustness of the approach in diverse environments.

In summary, this paper offers a meaningful contribution to the field of computer vision and robotics by presenting a viable path forward for efficient, scalable, and private map-sharing solutions in SfM applications.