LivePose: Immersive 3D Reconstruction in Real-Time from Single-Camera Video with Dynamic Camera Movements
Introduction:
In the field of 3D reconstruction from RGB images, the assumption of static camera poses has been widely accepted. However, with the increasing development of real-time methods for mobile devices, it is important to consider dynamic camera poses, which can change based on events such as bundle adjustment and loop closure. This aspect has been addressed in the RGB-D setting, but has been largely ignored in the RGB-only setting. To address this gap, we introduce a new task called online reconstruction from dynamically-posed images. To support further research in this area, we present the LivePose dataset, which contains dynamic poses from a SLAM system running on ScanNet. We also propose a novel de-integration module that effectively removes stale scene content. Our findings demonstrate the importance of responding to pose updates for high-quality reconstruction, and our de-integration framework offers an effective solution to this challenge.
Full Article: LivePose: Immersive 3D Reconstruction in Real-Time from Single-Camera Video with Dynamic Camera Movements
Dynamically-Posed Images: A New Task in Online Reconstruction
In the field of dense 3D reconstruction from RGB images, the assumption of static camera pose estimates has been prevalent. However, with the growing interest in real-time methods for mobile devices, this assumption no longer holds true for online execution. Real-time SLAM (Simultaneous Localization and Mapping) techniques can update camera poses dynamically following events like bundle adjustment and loop closure. While this challenge has been addressed in the RGB-D setting, it remains largely untreated in the RGB-only setting.
Introducing LivePose Dataset
To tackle this issue and support further research, a group of researchers has formalized the problem and defined the new task of online reconstruction from dynamically-posed images. As part of their efforts, they have created a dataset called LivePose, which contains dynamic poses from a SLAM system running on ScanNet. This dataset aims to provide valuable resources for researchers working on this new task.
Adapting Existing Reconstruction Systems
To demonstrate the significance of responding to pose updates for high-quality reconstruction, the researchers selected three recent reconstruction systems. They applied a framework based on de-integration to each system, adapting them to the dynamic-pose setting. This framework involves de-integrating past views and re-integrating them with the updated poses. By incorporating these pose updates into the reconstruction process, the researchers aimed to improve the quality and accuracy of the reconstructed scenes.
A Novel Approach: Non-Linear De-Integration
In addition to the adaptation framework, the researchers proposed a novel, non-linear de-integration module. This module is designed to learn how to remove stale scene content, ensuring that the reconstructed scenes remain up-to-date. By effectively removing irrelevant or outdated information, this module contributes to enhancing the overall quality of the reconstructed scenes.
The Effectiveness of the De-Integration Framework
The researchers conducted experiments to evaluate the effectiveness of their de-integration framework in dynamic-pose reconstruction. The results revealed that responding to pose updates is crucial for achieving high-quality reconstruction. By incorporating the proposed de-integration framework, the adapted reconstruction systems showed significant improvements in their ability to handle dynamically-posed images. This suggests that the de-integration framework is an effective solution to the challenges posed by dynamic camera poses in reconstruction tasks.
Conclusion
In conclusion, the assumption of static camera pose estimates in 3D reconstruction from RGB images is no longer sufficient for online execution, especially with the increasing popularity of real-time methods on mobile devices. The introduction of the new task of online reconstruction from dynamically-posed images marks an important development in this field. By utilizing the LivePose dataset and the de-integration framework, researchers are now able to tackle the challenges posed by dynamic camera poses and achieve high-quality reconstruction. These advancements have significant implications for various areas such as augmented reality, robotics, and virtual reality. Further research in this direction is expected to bring about more improvements in the field of online reconstruction.
Summary: LivePose: Immersive 3D Reconstruction in Real-Time from Single-Camera Video with Dynamic Camera Movements
In traditional 3D reconstruction from RGB images, static camera poses are assumed. However, this assumption is not valid for online execution, where poses are dynamic and can be updated. While this issue has been addressed in RGB-D settings, it remains largely untreated in RGB-only settings. To fill this gap, we introduce the task of online reconstruction from dynamically-posed images and present the LivePose dataset, which contains dynamic poses from a SLAM system running on ScanNet. We adapt three recent reconstruction systems to the dynamic-pose setting using a de-integration framework and propose a non-linear de-integration module to remove outdated scene content. Our results show that responding to pose updates is crucial for high-quality reconstruction, and our de-integration framework offers an effective solution.
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