\section{Introduction}
\label{sec:intro}

Virtually experiencing real environments improves their accessibility and enables a wide range of applications across diverse domains. Indoor settings, such as homes, offices, and other single- or multi-room environments, are of particular interest, since they constitute the primary spaces where people spend their time and carry out activities that can benefit from virtual access~\cite{Pintore:2024:A3M}. In real estate, for example, virtual tours allow potential buyers and renters to inspect properties without the need for physical travel, eventually also evaluating different arrangements and decorations~\cite{Sulaiman:2020:MVR}. In facility management and emergency response, they support familiarization with building locations, layouts, and connections, improving task execution performance~\cite{Boguslawski:2022:3BI}. Similar benefits extend to many other domains~\cite{Tukur:2025:PII}.

Of the available approaches to create virtual counterparts of real spaces, automatic reconstruction from captured data is the most practical, as manual modeling is costly and time-consuming to create, hard to maintain, and rarely reflects the true as-built or as-inhabited state of an environment. Image-based methods are especially critical, since visual information is key for location recognition.
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Among the many image-based capture modalities, ranging from commodity RGB cameras to RGB-D devices, $360^\circ$ (also called \emph{panoramic}, \emph{spherical}, \emph{omnidirectional}, or \emph{surround-view}) photography is emerging as an especially cost-effective and efficient solution. A single panoramic shot provides full coverage of the scene from the capture viewpoint, eliminating the need for multi-view setups and complex registration pipelines~\cite{Yang:2020:NRR}. Thanks to the widespread availability of 360$^\circ$ cameras, rapid data acquisition is now possible for both expert and casual users~\cite{Jokela:2019:HPU}. In particular, in the real estate industry, millions of users utilize consumer-grade cameras to generate and share virtual tours with minimal effort~\cite{Cruz:2021:ZID,Hutchcroft:2022:CCP}. 

Beyond acquisition efficiency, a $360^\circ$ image inherently affords a more dynamic exploration than a standard image, particularly when experienced through a head-mounted display, where natural head movements enable intuitive navigation of the field of view~\cite{Xu:2020:S3V}. For this reason, omnidirectional imagery has become a central medium for general~\cite{Matzen:2017:LSP} and indoor~\cite{Sulaiman:2020:MVR} interactive content generation. Furthermore, panoramas are easily shared across platforms and increasingly regarded as potential building blocks of persistent digital spaces~\cite{Dong:2022:MMC}.

Despite these advantages, panoramic imagery, whether viewed individually or as linked tours, also suffers from notable limitations. In typical indoor scenarios, essential tasks include establishing a \emph{sense of presence} through visual exploration and, for multi-room environments, gaining \emph{location awareness} by understanding the current room's placement within the larger environment, and navigating across rooms or through a floorplan~\cite{Zhang:2021:EHI}. Yet, a panorama captures only the appearance from a fixed point, omitting crucial spatial cues. The absence of stereopsis and motion parallax leads to a flattened perception of space~\cite{Waidhofer:2022:PTL}, a limitation particularly noticeable in indoor scenes, where nearby walls and objects make the lack of depth more apparent~\cite{Pintore:2024:A3M}. Furthermore, an effective navigation and localization also depends on explicit representations of the environment’s architectural structure~\cite{Zhang:2021:EHI}. Finally, many interactive applications, such as virtual staging, call for functionalities that go well beyond passive viewing of static panoramas, including the ability to clear spaces, refurnish interiors, or alter lighting conditions~\cite{Zhang:2016:ERR}.

This article presents an integrative and up-to-date review of methods that address these challenges, bridging research from machine learning, computer vision, and computer graphics. We maintain a public repository of resources accompanying this survey (\url{https://github.com/crs4/panostar}).
