With the rapid development of high-speed communication and artificial intelligence
technologies, human perception of real-world scenes is no longer limited to the use of small …

Estimating the accurate depth from a single image is challenging since it is inherently
ambiguous and ill-posed. While recent works design increasingly complicated and powerful …

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 …

We present HoHoNet, a versatile and efficient framework for holistic understanding of an
indoor 360-degree panorama using a Latent Horizontal Feature (LHFeat). The compact …

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 …

As an essential component for many autonomous driving and robotic activities such as ego-
motion estimation, obstacle avoidance and scene understanding, monocular depth …

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 …

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