![]() URI DOI 10.1007/978-0-7_31 Appears in Collections: AI_Conference Papers Files in This Item: There are no files associated with this item. Free pictures to download and use in your next project. We collect 30 test 360-Degree images exhibiting challenging reflection artifacts and demonstrate that the proposed method outperforms the existing state-of-the-art methods on 360-Degree images. 46,404 Free images of Reflection Find your perfect reflection image. We first search for the reference image of the reflected scene in a 360-degree image based on the reflection geometry, which is then used to guide the network to restore the faithful colors of the reflection image. We adopt the zero-shot learning scheme to avoid the burden of collecting paired data for supervised learning and the domain gap between different datasets. Zero-Shot Learning for Reflection Removal of Single 360-Degree Image. At the point of incidence where the incident ray strikes the mirror, a perpendicular line is drawn known as the Normal. The ray that leaves the mirror is known as Reflected Ray. In this paper, we attempt to remove such challenging reflection artifacts using 360-Degree images. The principle follows reflection alignment and transmission recovery and it could. In the diagram given above, the ray of light that approaches the mirror is known as Incident Ray. ![]() However, in many cases, real reflection artifacts are sharp and intensive enough such that even humans cannot completely distinguish between the transmitted and reflected scenes. Title Zero-Shot Learning for Reflection Removal of Single 360-Degree Image Author Han, Byeong-Ju Sim, Jae-Young Issue Date Publisher Springer Science and Business Media Deutschland GmbH Citation European Conference on Computer Vision, pp.533 - 548 Abstract The existing methods for reflection removal mainly focus on removing blurry and weak reflection artifacts and thus often fail to work with severe and strong reflection artifacts.
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