RGB-D data is essential for solving many problems in computer vision. Hundreds of public
RGB-D datasets containing various scenes, such as indoor, outdoor, aerial, driving, and …

Estimating the depths of equirectangular (ie, 360) images (EIs) is challenging given the
distorted 180 x 360 field-of-view, which is hard to be addressed via convolutional neural …

In this article, we investigate the problem of panoramic image reflection removal to relieve
the content ambiguity between the reflection layer and the transmission scene. Although a …

Monocular depth estimation is an ambiguous problem, thus global structural cues play an
important role in current data-driven single-view depth estimation methods. Panorama …

Single-view depth estimation from omnidirectional images has gained popularity with its
wide range of applications such as autonomous driving and scene reconstruction. Although …

In this work we introduce a biologically inspired long-range skip connection for the UNet
architecture that relies on the perceptual illusion of hybrid images, being images that …

Depth estimation, as a necessary clue to convert 2D images into the 3D space, has been
applied in many machine vision areas. However, to achieve an entire surrounding 360° …

Due to the increased demand of 360 images (eg virtual reality), estimating 360 depths via
deep learning has drawn attention recently. However, all previous studies share the same …

Panoramic distortion poses a significant challenge in 360 depth estimation, particularly
pronounced at the north and south poles. Existing methods either adopt a bi-projection …

360° images offer panoramic views of captured environments, placing users within an
egocentric perspective. While users can freely rotate their viewpoint, they don't experience 6 …