LivePose: Online 3D Reconstruction from Monocular Video with Dynamic Camera Poses (2304.00054v2)

Abstract: Dense 3D reconstruction from RGB images traditionally assumes static camera pose estimates. This assumption has endured, even as recent works have increasingly focused on real-time methods for mobile devices. However, the assumption of a fixed pose for each image does not hold for online execution: poses from real-time SLAM are dynamic and may be updated following events such as bundle adjustment and loop closure. This has been addressed in the RGB-D setting, by de-integrating past views and re-integrating them with updated poses, but it remains largely untreated in the RGB-only setting. We formalize this problem to define the new task of dense online reconstruction from dynamically-posed images. To support further research, we introduce a dataset called LivePose containing the dynamic poses from a SLAM system running on ScanNet. We select three recent reconstruction systems and apply a framework based on de-integration to adapt each one to the dynamic-pose setting. In addition, we propose a novel, non-linear de-integration module that learns to remove stale scene content. We show that responding to pose updates is critical for high-quality reconstruction, and that our de-integration framework is an effective solution.

• The paper introduces a novel task that integrates dynamic camera pose updates into online 3D reconstruction.
• It presents the LivePose dataset with SLAM-derived pose sequences to benchmark systems under real-world conditions.
• An innovative de-integration framework adapts RGB-only methods to achieve significant qualitative and quantitative improvements.

LivePose: Online 3D Reconstruction from Monocular Video with Dynamic Camera Poses

The paper "LivePose: Online 3D Reconstruction from Monocular Video with Dynamic Camera Poses" addresses a critical challenge in the domain of dense 3D reconstruction: the integration of dynamic camera pose estimates in real-time applications. This research identifies a fundamental oversight in existing RGB-only reconstruction methodologies, which conventionally assume static camera poses, thus neglecting real-world scenarios where pose estimates frequently undergo updates. The authors propose a novel approach to solve this problem by redefining the task itself and developing an innovative dataset and framework to support it.

Summary of Contributions

The paper introduces a significant shift in how 3D reconstruction from monocular videos should be approached, particularly stressing that accommodating dynamic camera pose updates is crucial for maintaining high fidelity in reconstructions. This is particularly important for real-time applications on mobile devices where pose updates from Simultaneous Localization and Mapping (SLAM) systems are frequent. The principal contributions of the paper include:

1. New Task Definition: The authors formalize a novel task—dense online 3D reconstruction from dynamically-posed RGB images—addressing the inherent challenges of real-time applications impacted by continuously updated SLAM pose estimates.
2. LivePose Dataset: To facilitate research in this area, the authors have released the LivePose dataset, which includes SLAM-derived dynamic pose sequences for the widely used ScanNet dataset. This dataset is critical for evaluating reconstruction systems in environments that approximate real-world conditions.
3. De-integration Framework: The research adapts pre-existing RGB-only reconstruction methods to handle the dynamic-pose setting using a general de-integration framework inspired by BundleFusion—a method developed for RGB-D data. The introduction of a novel, non-linear de-integration module demonstrates the ability to effectively respond to pose updates by learning to remove outdated scene content.
4. Evaluation and Validation: Empirical validation is conducted using three state-of-the-art reconstruction methods (Atlas, NeuralRecon, and DeepVideoMVS), where the adaptation of these methods to dynamic poses results in significant qualitative and quantitative improvements over existing solutions.

Implications and Future Directions

The implications of this research are multifaceted, spanning both academic and practical applications. By addressing dynamic pose updates, this work enhances the robustness and reliability of monocular 3D reconstruction systems in real-time scenarios, a necessity for interactive applications such as augmented reality (AR) on mobile devices. The approaches presented could be foundational for advancing real-time scene understanding, navigation, and reconstruction in dynamic environments.

The future directions proposed by this paper invite further exploration into more efficient de-integration strategies, potentially leveraging advancements in neural network architectures or more sophisticated algorithms for SLAM integration. Moreover, the existence of the LivePose dataset opens avenues for benchmarking future methods under dynamic conditions, thus propagating further innovations in this field.

This contribution provides a structured pathway for the development of more sophisticated 3D reconstruction models capable of functioning effectively in dynamic real-world environments, ensuring adaptability, accuracy, and consistency in the face of fluctuating camera poses—a common occurrence in practical deployments.