\section{Related surveys}
\label{sec:related}

Several well-established surveys have covered classic geometry reasoning methods for indoor reconstruction~\cite{Pintore:2020:SA3}, as well as more recent learning-based solutions~\cite{Patil:2024:ADA,Wang:2024:NEI}. Their focus, however, is mostly on analysis and synthesis from point-cloud or multi-view data, also covered in other surveys dedicated specifically to RGB-D input~\cite{Zollhofer:2018:S3R,Li:2022:HIS,Zhu:2024:SI3}. We cover, instead, the specifics of monocular analysis and 360$^\circ$ input and restrict the target to what is needed to transform input to an explorable model. 360$^\circ$ imagery is also covered in other recent surveys of general 3D scene reconstruction and understanding~\cite{DaSilveira:2022:3SG,Gao:2022:RPI} and immersive extended reality applications~\cite{Tukur:2025:PII}. The survey by Zou et al.~\cite{Zou:2021:MRL} provides a comparative study of state-of-the-art Manhattan-World layout reconstruction methods from a single 360$^\circ$ image, thereby covering a focused subset of our objectives. We build on their work for layout reconstruction, but our scope is broader: addressing environments beyond strict orthogonal wall assumptions and extending to research areas beyond layout reconstruction alone.

Reconstructing and exploring indoor environments from minimal panoramic input remains a challenging problem due to the limited depth and structural cues inherent in monocular imagery, the geometric and material complexity of indoor scenes, and the requirements of exploration-oriented applications. These challenges have given rise to several distinct research directions within 3D reconstruction and omnidirectional image analysis, which have been addressed from different perspectives in existing survey literature.

Structured indoor scene reconstruction has been extensively studied in the classical survey by Pintore et al.~\cite{Pintore:2020:SA3}, which focused on recovering approximate structured geometry linked to visual representations from sparse and incomplete observations. These observations are mainly derived from multi-view imagery or point clouds and analyzed through geometric reasoning. Beyond detailing the core challenges of structure extraction, this work highlighted the role of indoor-specific priors (especially geometric and structural ones) and critically examined the limitations of purely geometric approaches. More recent surveys have extended this analysis to more recent learning-based methods that exploit data-driven priors~\cite{Patil:2024:ADA,Wang:2024:NEI}. However, these reviews also emphasize multi-view or point-cloud data, often supported by RGB-D sensors, whose use has been comprehensively reviewed elsewhere~\cite{Zollhofer:2018:S3R,Li:2022:HIS,Zhu:2024:SI3}. In contrast, our work focuses on monocular $360^\circ$ imagery for indoor environments, as its intrinsic properties yield a distinct and well-defined range of methodological solutions. For instance, the 360$^\circ$ field of view enables holistic single-image reasoning and often supports more consistent reconstructions than conventional pinhole imagery, while simultaneously introducing distinctive challenges related to geometric distortion, non-uniform sampling, and large-resolution data processing.

Panoramic imagery has been examined in broader surveys on 3D scene reconstruction and understanding~\cite{Gao:2022:RPI,Yu:2023:ADL} and in the context of immersive extended reality applications~\cite{Tukur:2025:PII}. Although these studies establish the importance and distinctive properties of omnidirectional data, they do not specifically address single-view indoor reconstruction or exploration. In particular, Gao et al.~\cite{Gao:2022:RPI} review panoramic imaging principles, system architectures, and applications such as autonomous driving and robotics, while Tukur et al.~\cite{Tukur:2025:PII} provide a scoping review of technological frameworks, applications, and limitations of panoramic-based extended reality (XR) systems. We partially build on these surveys for general concepts and applications. Yu et al.~\cite{Yu:2023:ADL}, instead, investigate the application of deep learning to the specific configuration of top-view omnidirectional imagery. Although this setup can support structural inference indoors, the surveyed applications primarily target ambient-assisted living and surveillance, thereby complementing the works reviewed here, emphasizing human and object detection and pose estimation rather than scene exploration.

The survey by Da Silveira et al.~\cite{DaSilveira:2022:3SG} analyzed methods for 3D scene geometry recovery from one, two, or multiple panoramas across indoor and outdoor environments. Building on this foundation, our work revisits single-panorama depth and layout estimation with an updated view of recent advances and substantially broadens the scope to include multi-room reconstruction, visual data completion, and exploration-oriented methods. Layout reconstruction itself has been the focus of a comparative study by Zou et al.~\cite{Zou:2021:MRL}, which systematically evaluated Manhattan-world layout recovery from a single panorama (i.e., assuming orthogonality among walls, floor, and ceiling; see~\autoref{sec:background:priors}). We extend beyond these assumptions to address non-Manhattan indoor scenes and also expand the scope from pure static monocular reconstruction toward dynamic exploration. Additionally, we update and unify the analyses of these two classical surveys by incorporating recent trends, including the growing prominence of generative and diffusion-based models and the emergence of foundation models.

Several recent surveys partially address these emerging directions. Meng et al.~\cite{Meng:2025:ISG} review methods for 3D indoor scene geometry recovery from omnidirectional images, with a focus on depth estimation, single-room layout reconstruction, and object recovery. Our work expands the analysis of these topics, while also addressing multi-room reconstruction, navigation in complex indoor environments, and the generation of interactive exploration experiences. Ai et al.~\cite{Ai:2025:SRL} provide a broad analysis of omnidirectional image processing from a representation learning perspective, covering imaging principles, learned representations, optimization strategies, and a taxonomy of deep learning methods across tasks and applications. While providing valuable context on omnidirectional vision, this survey does not specifically address single-view 3D indoor geometry or exploration. Nevertheless, its detailed analysis of representation learning concepts offers complementary insights that enrich and contextualize the scope of our survey.
