This article provides a comprehensive survey on pioneer and state-of-the-art 3D scene
geometry estimation methodologies based on single, two, or multiple images captured …

Depth estimation from a monocular 360 image is an emerging problem that gains popularity
due to the availability of consumer-level 360 cameras and the complete surrounding …

A well-known challenge in applying deep-learning methods to omnidirectional images is
spherical distortion. In dense regression tasks such as depth estimation, where structural …

Abstract 360deg cameras can capture complete environments in a single shot, which makes
360deg imagery alluring in many computer vision tasks. However, monocular depth …

Learning depth from spherical panoramas is becoming a popular research topic because a
panorama has a full field-of-view of the environment and provides a relatively complete …

We introduce a novel self-supervised pretext task for learning representations from audio-
visual content. Prior work on audio-visual representation learning leverages …

We introduce a method to convert stereo 360^ ∘ (omnidirectional stereo) imagery into a
layered, multi-sphere image representation for six degree-of-freedom (6DoF) rendering …

Learning based approaches for depth perception are limited by the availability of clean
training data. This has led to the utilization of view synthesis as an indirect objective for …

Omnidirectional images and videos are widespread due to the popularization of devices for
capturing and visualization. Unlike pinhole-based imagery, omnidirectional media lie on the …

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 …