Titlebook: Computer Vision – ECCV 2022; 17th European Confer Shai Avidan,Gabriel Brostow,Tal Hassner Conference proceedings 2022 The Editor(s) (if app

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,PanoFormer: Panorama Transformer for Indoor 360, Depth Estimation, panoramic structures efficiently due to the fixed receptive field in CNNs. This paper proposes the .rama trans. (named .) to estimate the depth in panorama images, with tangent patches from spherical domain, learnable token flows, and pano-rama specific metrics. In particular, we divide patches on

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,Self-supervised Human Mesh Recovery with Cross-Representation Alignment,ted benchmark datasets. Recent progress in self-supervised human mesh recovery has been made using synthetic-data-driven training paradigms where the model is trained from synthetic paired 2D representation (.., 2D keypoints and segmentation masks) and 3D mesh. However, on synthetic dense correspond

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,A Reliable Online Method for Joint Estimation of Focal Length and Camera Rotation,online, but these estimates can be unreliable due to irregularities in the scene, uncertainties in line segment estimation and background clutter. Here we address this challenge through four initiatives. First, we use the PanoContext panoramic image dataset [.] to curate a novel and realistic datase

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0302-9743 tion; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation..978-3-031-19768-0978-3-031-19769-7Series ISSN 0302-9743 Series E-ISSN 1611-3349