360 images represent scenes captured in all possible viewing directions. They enable viewers to navigate freely around the scene and thus provide an immersive experience. Conversely, conventional images represent scenes in a single viewing direction with a small or limited field of view (FOV). As a result, only some parts of the scenes are observed, and valuable information about the surroundings is lost. In this paper, we propose a learning-based approach that reconstructs the scene in 360 x 180 from a sparse set of conventional images (typically 4 images). The proposed approach first estimates the FOV of input images relative to the panorama. The estimated FOV is then used as the prior for synthesizing a high-resolution 360 panoramic output. The proposed method overcomes the difficulty of learning-based approach in synthesizing high resolution images (up to 512 x 1024). Experimental results demonstrate that the proposed method produces 360 panorama with reasonable quality. It is shown that it outperforms the alternative method and can be generalize for non-panorama scene and images captured by a smartphone camera.
Xiao al @cite_3 predicted the viewpoint of a given panoramic observation. The prediction generates rough panorama structure and compass-like prediction in determining the location of the viewpoint in 360 . Georgoulis al @cite_20 estimated environmental map from the reflectance properties of input images. They utilized these properties from a foreground object to estimate the background environment in a panoramic representation. In contrast to these works, ours does not rely on reference images as the input, and it synthesizes actual panoramic imagery.
@cite_20 @cite_3
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How much does a single image reveal about the environment it was taken in? In this paper, we investigate how much of that information can be retrieved from a foreground object, combined with the background (i.e. the visible part of the environment). Assuming it is not perfectly diffuse, the foreground object acts as a complexly shaped andfar-from-perfect mirror An additional challenge is that its appearance confounds the light coming from the environment with the unknown materials it is made of. We propose a learning-based approach to predict the environment from multiple reflectance maps that are computed from approximate surface normals. The proposed method allows us to jointly model the statistics of environments and material properties. We train our system from synthesized training data, but demonstrate its applicability to real-world data. Interestingly, our analysis shows that the information obtained from objects made out of multiple materials often is complementary and leads to better performance. We introduce the problem of scene viewpoint recognition, the goal of which is to classify the type of place shown in a photo, and also recognize the observer's viewpoint within that category of place. We construct a database of 360° panoramic images organized into 26 place categories. For each category, our algorithm automatically aligns the panoramas to build a full-view representation of the surrounding place. We also study the symmetry properties and canonical viewpoint of each place category. At test time, given a photo of a scene, the model can recognize the place category, produce a compass-like indication of the observer's most likely viewpoint within that place, and use this information to extrapolate beyond the available view, filling in the probable visual layout that would appear beyond the boundary of the photo.