3D Reconstruction with Spatial Memory (2408.16061v1)

Abstract: We present Spann3R, a novel approach for dense 3D reconstruction from ordered or unordered image collections. Built on the DUSt3R paradigm, Spann3R uses a transformer-based architecture to directly regress pointmaps from images without any prior knowledge of the scene or camera parameters. Unlike DUSt3R, which predicts per image-pair pointmaps each expressed in its local coordinate frame, Spann3R can predict per-image pointmaps expressed in a global coordinate system, thus eliminating the need for optimization-based global alignment. The key idea of Spann3R is to manage an external spatial memory that learns to keep track of all previous relevant 3D information. Spann3R then queries this spatial memory to predict the 3D structure of the next frame in a global coordinate system. Taking advantage of DUSt3R's pre-trained weights, and further fine-tuning on a subset of datasets, Spann3R shows competitive performance and generalization ability on various unseen datasets and can process ordered image collections in real time. Project page: \url{https://hengyiwang.github.io/projects/spanner}

• The paper presents Spann3R, enabling dense 3D reconstruction from unordered image collections without needing prior camera calibration.
• It leverages transformer-based encoders and decoders with a spatial memory to predict and update global 3D geometry in real time.
• Evaluations on multiple datasets demonstrate its efficiency with real-time performance at over 50 fps and competitive accuracy against existing methods.

3D Reconstruction with Spatial Memory

The paper "3D Reconstruction with Spatial Memory" by Hengyi Wang and Lourdes Agapito introduces a novel method for dense 3D reconstruction from ordered or unordered image collections without prior knowledge of the camera parameters or the scene. The proposed approach, named Spann3R, builds upon the DUSt3R framework and employs a transformer-based architecture to innovate in the field of computer vision by regressing pointmaps without optimization-based alignment.

Technical Summary

Spann3R leverages an external spatial memory to track previous 3D information and predict the 3D structure of new frames in a global coordinate system. The key advancement over DUSt3R is eliminating the need for per-image pair optimization, allowing for real-time incremental reconstruction. The method uses a transformer-based network that encodes visual and geometric features into a structured memory system, enabling robust 3D geometry prediction through memory queries.

The architecture comprises ViT encoders and intertwined decoders for feature encoding and decoding, respectively. The target decoder produces query features for memory readout, while the reference decoder reconstructs geometry based on retrieved memory features. The memory encoder processes past frame predictions to update the spatial memory, allowing subsequent frames to be decoded in a consistent global coordinate system.

Results

The performance of Spann3R is evaluated on multiple indoor and outdoor datasets, including 7Scenes and NRGBD for indoor evaluation and DTU for object-level reconstruction. Spann3R demonstrates competitive reconstruction quality, matching or surpassing the performance of existing methods like FrozenRecon and DUSt3R in several metrics, including accuracy and completion.

Key results include:

• Real-Time Processing: Spann3R achieves over 50 frames per second (fps) in online reconstruction scenarios, facilitating applications requiring real-time processing.
• Accuracy: On the 7Scenes dataset, Spann3R shows an accuracy (mean) of 0.0342, compared to 0.0286 for DUSt3R, with a median accuracy of 0.0148.
• Generalization: The method generalizes well across various unseen datasets, proving its robustness and applicability to real-world scenarios.

Practical and Theoretical Implications

The paper's contributions have several significant practical and theoretical implications:

1. End-to-End Framework: Eliminating the need for optimization-based global alignment streamlines the 3D reconstruction pipeline, making it more robust and scalable for real-time applications.
2. Incremental Reconstruction: The ability to incrementally update the 3D model in real-time is crucial for applications such as autonomous driving, robotics, and augmented reality, where rapid and continuous environmental understanding is required.
3. Scalability: The memory management strategy ensures that the approach scales effectively with longer sequences, maintaining reconstruction quality without excessive computational overhead.

Future Directions

Given the robust performance and promising results of Spann3R, several future research directions are suggested:

• Scalability Enhancements: Extending the model to handle larger-scale scenes and more complex environments would be beneficial. Strategies such as hierarchical memory systems or more efficient encoding schemes could be explored.
• Self-Supervised Learning: Incorporating self-supervised learning techniques could enable training on unannotated video data, broadening the applicability and reducing dependency on labeled datasets.
• Integration with Classical Techniques: The integration of classical computer vision techniques like bundle adjustment for refining the reconstructed geometry poses an intriguing area of research. This hybrid approach could mitigate accumulated errors and enhance the robustness of the reconstruction.

Conclusion

Spann3R represents a significant advancement in the field of 3D reconstruction by introducing a framework that combines transformer-based networks with spatial memory. The resulting method enables real-time, accurate, and scalable 3D reconstruction from image collections, with broad implications for various applications in computer vision and adjacent domains. The future development of Spann3R could pioneer new capabilities in automated scene understanding and model reconstruction.