But, our current design has a limitation: the transition direction and distance are fixed, resulting in equally spaced distances between viewports.

Extensive experimental results demonstrate that Assessor360 outperforms state-of-the-art methods on multiple OIQA datasets.

Omnidirectional (or 360-degree) images are increasingly being used for 3D applications since they allow the rendering of an entire scene with a single image.

Omnidirectional (or 360-degree) images are increasingly being used for 3D applications since they allow the rendering of an entire scene with a single image. Existing works based on neural radiance fields demonstrate successful 3D reconstruction quality on egocentric videos, yet they suffer from long training and rendering times. Recently, 3D Gaussian splatting has gained attention for its fast optimization and real-time rendering.

Omnidirectional (or 360-degree) images are increasingly being used for 3D applications since they allow the rendering of an entire scene with a single image.

But, our current design has a limitation: the transition direction and distance are fixed, resulting in equally spaced distances between viewports.

Omnidirectional videos that capture the entire surroundings are employed in a variety of fields such as VR applications and remote sensing. However, their wide field of view often causes unwanted objects to appear in the videos. This problem can be addressed by video inpainting, which enables the natural removal of such objects while preserving both spatial and temporal consistency. Nevertheless, most existing methods assume processing ordinary videos with a narrow field of view and do not tackle the distortion in equirectangular projection of omnidirectional videos. To address this issue, this paper proposes a novel deep learning model for omnidirectional video inpainting, called Distortion-Aware Omnidirectional Video Inpainting (DAOVI). DAOVI introduces a module that evaluates temporal motion information in the image space considering geodesic distance, as well as a depth-aware feature propagation module in the feature space that is designed to address the geometric distortion inherent to omnidirectional videos. The experimental results demonstrate that our proposed method outperforms existing methods both quantitatively and qualitatively.

Omnidirectional (or 360-degree) images are increasingly being used for 3D applications since they allow the rendering of an entire scene with a single image. Existing works based on neural radiance fields demonstrate successful 3D reconstruction quality on egocentric videos, yet they suffer from long training and rendering times. Recently, 3D Gaussian splatting has gained attention for its fast optimization and real-time rendering. For each Gaussian, we define a tangent plane that touches the unit sphere and is perpendicular to the ray headed toward the Gaussian center. We then leverage a perspective camera rasterizer to project the Gaussian onto the corresponding tangent plane.

Omnidirectional image super-resolution (ODISR) aims to upscale low-resolution (LR) omnidirectional images (ODIs) to high-resolution (HR), addressing the growing demand for detailed visual content across a $180^{\circ}\times360^{\circ}$ viewport. Existing methods are limited by simple degradation assumptions (e.g., bicubic downsampling), which fail to capture the complex, unknown real-world degradation processes. Recent diffusion-based approaches suffer from slow inference due to their hundreds of sampling steps and frequent pixel-latent space conversions. To tackle these challenges, in this paper, we propose RealOSR, a novel diffusion-based approach for real-world ODISR (Real-ODISR) with single-step diffusion denoising. To sufficiently exploit the input information, RealOSR introduces a lightweight domain alignment module, which facilitates the efficient injection of LR ODI into the single-step latent denoising. Additionally, to better utilize the rich semantic and multi-scale feature modeling ability of denoising UNet, we develop a latent unfolding module that simulates the gradient descent process directly in latent space. Experimental results demonstrate that RealOSR outperforms previous methods in both ODI recovery quality and efficiency. Compared to the recent state-of-the-art diffusion-based ODISR method, OmniSSR, RealOSR achieves significant improvements in visual quality and over \textbf{200$\times$} inference acceleration. Our code and models will be released.