Existing computer vision systems can compete with humans in understanding the visible
parts of objects, but still fall far short of humans when it comes to depicting the invisible parts …

We introduce a novel end-to-end deep-learning solution for rapidly estimating a dense
spherical depth map of an indoor environment. Our input is a single equirectangular image …

Nowadays 360° cameras, capable to capture full environments in a single shot, are
increasingly being used in a variety of Extended Reality (XR) applications that require …

We present a new data-driven approach for extracting geometric and structural information
from a single spherical panorama of an interior scene, and for using this information to …

Diminished reality (DR) refers to the removal of real objects from the environment by virtually
replacing them with their background. Modern DR frameworks use inpainting to hallucinate …

Abstract Today's Extended Reality (XR) applications that call for specific Diminished Reality
(DR) strategies to hide specific classes of objects are increasingly using 360° cameras …

Multi-layer images are currently the most prominent scene representation for viewing natural
scenes under full-motion parallax in virtual reality. Layers ordered in diopter space contain …

We present an end-to-end deep learning framework for indoor panoramic image inpainting.
Although previous inpainting methods have shown impressive performance on natural …

In this work, we present an AI-based Augmented Reality (AR) system for indoor planning
and refurbishing applications. AR can be an important medium for such applications, as it …

We introduce an innovative approach to automatically generate and explore immersive
stereoscopic indoor environments derived from a single monoscopic panoramic image in an …