arXiv:2411.18650v1 Announce Type: new Abstract: There has been extensive progress in the reconstruction and generation of 4D scenes from monocular casually-captured video. While these tasks rely heavily on known camera poses, the problem of finding such poses using structure-from-motion (SfM) often depends on robustly separating static from dynamic parts of a video. The lack of a robust solution to this problem limits the performance of SfM camera-calibration pipelines. We propose a novel approach to video-based motion segmentation to identify the components of a scene that are moving w.r.t. a fixed world frame. Our simple but effective iterative method, RoMo, combines optical flow and epipolar cues with a pre-trained video segmentation model. It outperforms unsupervised baselines for motion segmentation as well as supervised baselines trained from synthetic data. More importantly, the combination of an off-the-shelf SfM pipeline with our segmentation masks establishes a new state-of-the-art on camera calibration for scenes with dynamic content, outperforming existing methods by a substantial margin.
The article “Robust Motion Segmentation for Camera Calibration in 4D Scene Reconstruction” addresses the challenge of accurately reconstructing and generating 4D scenes from monocular video footage. While this process heavily relies on known camera poses, accurately determining these poses using structure-from-motion (SfM) techniques is hindered by the difficulty of robustly separating static and dynamic elements in the video. This limitation negatively impacts the performance of SfM camera-calibration pipelines. In response, the authors propose a novel approach called RoMo, which combines optical flow, epipolar cues, and a pre-trained video segmentation model to effectively identify the moving components of a scene relative to a fixed world frame. The RoMo method not only outperforms unsupervised and supervised baselines for motion segmentation, but also establishes a new state-of-the-art in camera calibration for scenes with dynamic content, surpassing existing methods by a significant margin.
Reconstructing 4D Scenes: Unleashing the Power of RoMo
Recently, there have been significant advancements in the reconstruction and generation of 4D scenes from monocular casually-captured video. This breakthrough has opened up new possibilities in various fields, including virtual reality, augmented reality, and computer vision. However, a crucial challenge in this process lies in finding accurately estimated camera poses using structure-from-motion (SfM).
SfM heavily relies on robustly separating static from dynamic parts of a video to determine camera poses. The problem arises when the scene contains both static and dynamic components, and no efficient solution exists to handle this scenario. As a result, the performance of SfM camera-calibration pipelines is limited, hindering progress in the reconstruction and generation of 4D scenes. This is where our proposed solution, RoMo, steps in to revolutionize the field.
A Novel Approach: RoMo
RoMo introduces a novel approach to video-based motion segmentation, allowing for the identification of moving components in a scene with respect to a fixed world frame. What sets RoMo apart is its simplicity and effectiveness in solving this complex problem.
Our approach combines two essential elements: optical flow and epipolar cues, along with a pre-trained video segmentation model. By iteratively refining the segmentation using these cues, RoMo achieves remarkable accuracy in motion segmentation.
Surpassing Baselines
When compared to unsupervised baselines for motion segmentation, RoMo outperforms them, showcasing its robustness and superiority. Additionally, RoMo surpasses supervised baselines trained from synthetic data, highlighting its ability to handle real-world scenarios effectively.
Unlocking New Possibilities: State-of-the-Art Camera Calibration
Most notably, combining RoMo’s segmentation masks with an off-the-shelf SfM pipeline establishes a new state-of-the-art in camera calibration for scenes with dynamic content. This groundbreaking innovation outperforms existing methods by a substantial margin.
The implications of this advancement are immense. We can now achieve more accurate camera calibration in scenarios where the scene contains both static and dynamic elements. This opens up exciting possibilities for augmented reality applications, where precise camera calibration is crucial for seamless integration of virtual content into the real world.
Moreover, the improved camera calibration offered by RoMo can greatly benefit virtual reality experiences. With better calibration, virtual environments can be rendered with increased precision, enhancing the overall immersion and realism for users.
In Conclusion
The introduction of RoMo as a solution for video-based motion segmentation brings us one step closer to unlocking the full potential of reconstructing 4D scenes. Its simplicity, effectiveness, and ability to outperform existing methods make it a game-changer in the field of camera calibration. With RoMo, we are not only improving the accuracy of camera poses but also paving the way for more innovative and immersive experiences in virtual reality and augmented reality.
The paper titled “Robust Motion Segmentation for Camera Calibration in Dynamic Scenes” addresses the challenge of accurately reconstructing and generating 4D scenes from monocular casually-captured videos. These tasks heavily rely on knowing the camera poses, which are typically obtained through structure-from-motion (SfM) techniques. However, accurately separating static and dynamic parts of a video remains a major challenge in SfM, limiting the performance of camera calibration pipelines.
To tackle this problem, the authors propose a novel approach called RoMo (Robust Motion Segmentation). RoMo combines optical flow and epipolar cues with a pre-trained video segmentation model to identify the components of a scene that are moving with respect to a fixed world frame. The iterative nature of RoMo allows it to effectively separate dynamic content from static content in a video.
The results of their experiments demonstrate that RoMo outperforms both unsupervised and supervised baselines for motion segmentation. Even when compared to supervised baselines trained on synthetic data, RoMo consistently achieves better performance. This indicates the effectiveness of their approach in accurately identifying moving objects in a scene.
Furthermore, the authors highlight that integrating RoMo’s segmentation masks with an off-the-shelf SfM pipeline leads to a new state-of-the-art in camera calibration for scenes with dynamic content. The performance improvement over existing methods is significant, indicating the practical value of their approach.
Overall, this paper presents a promising solution to the problem of motion segmentation in camera calibration pipelines. By effectively separating dynamic content from static content, RoMo enables more accurate reconstruction and generation of 4D scenes from monocular videos. Future research could explore the application of RoMo in other computer vision tasks and investigate potential improvements to further enhance its performance.
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