\section{Survey scope}
\label{sec:scope}

This work examines methods for automatically transforming indoor panoramic shots into representations that support structural and geometric recovery, dynamic exploration, and basic editing. Our focus lies on techniques that operate mostly from a \emph{single panoramic image}, covering both \emph{analysis} (for model reconstruction) and \emph{synthesis} (for immersive viewing, navigation, and editing).

\paragraph*{Motivation} The emphasis on monocular panoramas is driven by both practical relevance and methodological challenges. 
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From a practical standpoint, consumer-grade 360$^\circ$ cameras have become widely available and have transformed industries such as real estate through crowd-sourcing techniques. Nowadays, most interiors are typically documented by asking individual agents or casual users to take a single panoramic picture in the center of each room. For example, users of the RICOH Theta series alone have already produced and shared over one hundred million panoramas~\cite{Shabani:2021:ESF}. Techniques capable of enriching this massive existing corpus, without requiring new captures, therefore carry enormous potential for real-world applications.
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From a scientific and technical standpoint, monocular analysis and exploration pose unique challenges that warrant specialized treatment. Reconstructing geometry and structure from a single image is inherently an ill-posed problem, requiring inference under strong ambiguity and relying heavily on architectural assumptions and data-driven priors, without the possibility to exploit multi-view consistency constraints or direct geometric evidence. Moreover, immersive exploration and editing of single panoramas demand specialized methods to generate parallax cues for head-mounted displays, while also opening opportunities for streamlined, image-based editing workflows.

\paragraph*{Content} In the following, after providing the relevant background (\autoref{sec:background}), we first examine automatic modeling solutions that enrich panoramas with geometric, structural, and semantic information. This includes the progression from per-pixel depth estimation to the recovery of room layouts and inter-room connectivity under sparse interior sampling (\autoref{sec:geostruct}). We then explore how these enriched representations enable dynamic interaction, discussing view-synthesis methods for stereo parallax and motion within and across rooms, as well as techniques for scene customization, such as styling and refurnishing (\autoref{sec:interactive}). Next, we discuss how foundation and large pre-trained models provide a promising direction for single-panorama inference by leveraging broad prior knowledge and reducing reliance on task-specific datasets (\autoref{sec:foundation}).. Finally, we review practical applications (\autoref{sec:applications}) and identify open research challenges (\autoref{sec:challenges}).

\paragraph*{Selection criteria} This article surveys recent work from major graphics, vision, and machine learning journals and conference proceedings, complemented by relevant preprints. Papers were selected for importance and relevance, relying on our judgment to provide a structured overview of key advances, without claiming completeness. Rather than exhaustive coverage, we highlight representative and seminal contributions and emphasize the links between modeling, synthesis, and immersive exploration. By situating these works within a unified perspective on panoramic imaging research, we aim to clarify how diverse research directions converge to advance the understanding and use of panoramic imaging.
