Omnidirectional Local Radiance Fields for Photorealistic View Synthesis from Dynamic 360° Videos

Core Concepts

A novel approach called Omnidirectional Local Radiance Fields (OmniLocalRF) that can render static-only scene views, removing and inpainting dynamic objects simultaneously from omnidirectional videos.

Abstract

The paper introduces a new method called Omnidirectional Local Radiance Fields (OmniLocalRF) for photorealistic view synthesis from dynamic 360° videos. The key highlights are: OmniLocalRF combines the principles of local radiance fields with bidirectional optimization of omnidirectional rays to effectively remove and inpaint dynamic objects while performing large-scale omnidirectional view synthesis. It develops a multi-resolution motion mask prediction module that accurately segments dynamic objects in 360° videos without requiring a pretrained model. It proposes a camera pose estimation technique based on local view synthesis of 360° videos, which is robust in the presence of dynamic objects. Experiments show that OmniLocalRF outperforms existing methods in both qualitative and quantitative metrics, especially in complex real-world scenes. It eliminates the need for manual interaction, making it an effective and efficient solution.

Stats

Omnidirectional cameras capture continuous scene information across multiple frames, allowing bidirectional evaluation of samples taken from distant frames. Existing methods struggle to apply to omnidirectional input due to the inevitable presence of dynamic objects, including the photographer, in the wide field of view.

Quotes

"We introduce a new approach called Omnidirectional Local Radiance Fields (OmniLocalRF) that can render static-only scene views, removing and inpainting dynamic objects simultaneously." "Our approach combines the principles of local radiance fields with the bidirectional optimization of omnidirectional rays."

Key Insights Distilled From

OmniLocalRF

by Dongyoung Ch... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00676.pdf

Deeper Inquiries

How could the proposed method be extended to handle completely occluded regions that are not visible in the input videos

To handle completely occluded regions that are not visible in the input videos, the proposed method could be extended by incorporating additional information or priors. One approach could be to leverage contextual information from neighboring frames or scenes to infer the content of occluded regions. This could involve using techniques like inpainting algorithms or generative models to predict the appearance of occluded areas based on surrounding context. By integrating these methods into the model, it can effectively fill in missing information in completely occluded regions, enhancing the overall reconstruction quality.

How could the motion mask prediction be further improved to handle inefficient oversampling near polar regions in equirectangular space

To improve the motion mask prediction and address inefficient oversampling near polar regions in equirectangular space, several strategies can be implemented. One approach is to adjust the sampling strategy in polar regions to ensure more uniform coverage and reduce redundancy. This could involve using adaptive sampling techniques that allocate more samples in areas with higher information content and fewer samples in redundant regions. Additionally, incorporating spatial attention mechanisms or adaptive sampling algorithms can help optimize the sampling process and improve the accuracy of motion mask predictions in polar regions.

What additional components could be integrated into the pose estimation to enable more robust and accurate global bundle adjustment and loop closure

To enable more robust and accurate global bundle adjustment and loop closure in pose estimation, additional components can be integrated into the existing framework. One key component could be incorporating robust optimization techniques, such as robust bundle adjustment algorithms, to handle outliers and noisy data more effectively. By integrating robust optimization methods, the pose estimation process can be more resilient to errors and uncertainties in the input data. Furthermore, incorporating loop closure detection algorithms based on visual odometry or SLAM techniques can help refine camera trajectories and improve the overall accuracy of pose estimation. By integrating these components, the pose estimation system can achieve higher precision and reliability in complex environments.