\section{Novel view synthesis and immersive model exploration}
\label{sec:interactive}

While geometric and structural descriptions provide a strong foundation, they are not enough for interactive use. We review techniques for exploration from a single enriched panorama, including methods that recover stereo cues and motion parallax and support advanced navigation in sparsely-sampled multi-room environments (\autoref{sec:interactive:viewsynth}), and approaches that go beyond plain navigation (\autoref{sec:interactive:editing}).

%===========================================================================================
\subsection{View synthesis from a single panorama}
\label{sec:interactive:viewsynth}

Although a single-shot panorama offers an attractive way to capture and replicate real environments, it inherently limits content to what is visible from a fixed viewpoint~\cite{Waidhofer:2022:PTL}. This restriction reduces degrees of freedom to pure rotation about the panorama center, leading to artifacts and perceptual limitations. In particular, binocular disparity and motion parallax, which are two key components of immersive VR, are absent, causing indoor panoramas to appear flat. Moreover, clutter and occlusions often prompt users not only to change their viewing angle but also to attempt translational movements to peek around objects or structural elements, but such data is not present in the original panorama~\cite{Matzen:2017:LSP}. To achieve convincing immersion, therefore, systems must respond to viewpoint translation as well as rotation, making novel-view synthesis from a single panorama a central research challenge~\cite{Waidhofer:2022:PTL}. 

This involves combining occlusion-aware reprojection to present to the viewer the originally captured surfaces at the correct image location, with the completion of the image in the disoccluded areas. While augmenting panoramas with depth, using one of the techniques illustrated in~\autoref{sec:geostruct:depth}, significantly helps reprojection, novel solutions must be introduced for completion. The problem is complex, as not only must the newly generated content be plausible and consistent with the originally sampled data, but it must be consistent throughout time during interactive exploration. 
%
In particular, the appropriate strategies are fundamentally determined by the extent of the newly exposed regions induced by viewpoint motion, and thus by the nature and complexity of the inference required to plausibly fill the missing content. 

For \emph{small-to-medium viewpoint displacements}, such as those induced by stereo or head motion for limited parallax, most of the visible content remains close to what was originally observed. Disoccluded regions are typically narrow and localized, often appearing along object boundaries or depth discontinuities. As a result, infilling can rely heavily on nearby visual evidence, geometric consistency, and depth-aware reprojection, requiring relatively limited semantic or structural inference. Methods in this category, thus, have to primarily focus on accurate reprojection and lightweight completion to maintain visual coherence while minimizing artifacts during interactive viewing. Moreover, the constrained motion amount also opens the door to specific optimization meant to produce compact and efficient restricted 3D representations. We provide an overview of this class of methods in~\autoref{sec:interactive:viewsynth:small}.

In contrast, \emph{large viewpoint displacements} introduce extensive disocclusion, revealing substantial portions of the scene that were entirely unseen in the original panorama. In this regime, infilling cannot be guided solely by local image evidence, as large areas of missing content must be synthesized without direct observations. Consequently, these methods must make stronger assumptions and rely on richer scene representations, such as explicit 3D reconstructions, learned priors over indoor geometry, or generative models capable of hallucinating plausible structure and appearance. The increased reliance on inference and semantic understanding makes large-motion approaches inherently more complex, not only in terms of methods and generated representations, but also necessary to support unrestricted navigation and room-scale exploration. We provide an overview of this class of methods in~\autoref{sec:interactive:viewsynth:large}.

Building on these techniques, specialized methods have also been introduced to handle \emph{room-to-room navigation}, where viewpoint changes can span multiple connected spaces. These approaches must not only synthesize previously unseen content within individual rooms but also ensure spatial and visual consistency across room boundaries, often requiring integration of global scene layout to generate plausible and navigable transitions between rooms. We summarize this class of approaches in~\autoref{sec:interactive:viewsynth:multiroom}.

%----------------------------------------------
\begin{table*}[t]
\small
\centering
\begingroup
\setlength{\tabcolsep}{5pt}
\renewcommand{\arraystretch}{1.0}
%\newcommand{\rcol}[1]{\raggedright #1\par}
\rowcolors{2}{tbloddrowcolor}{tblevnrowcolor}
\begin{tabular}{|p{1.7cm} p{3.5cm} p{2.6cm} p{1.4cm} p{3.8cm} p{2.6cm}|}
\hline
\rowcolor{tblheadcolor}
\textbf{Family} & \textbf{Core idea} & \textbf{Representation} & \textbf{Range} & \textbf{Characteristics} & \textbf{Represent. methods} \\
\hline
Depth estimation, reprojection, and infilling
& {Infer per-pixel depth from RGB panorama; reproject to target view; fill holes}
& Offline depth estimation, online computation from RGB-D
& Small
& {Low memory overhead; fast rendering; per-frame infilling valid only for small disocclusions; requires temporal smoothing}
& \cite{Zhao:2013:C2V,Huang:2017:6VV,Tukur:2023:SFP} \\
Layout/semantic-guided IBR
& {View synthesis from reprojected RGB-D}
& Infer per-pixel depth from RGB panorama; reproject and synthesize target-view image
& Small
& {Low memory overhead; fast rendering; data-driven completion consistent with room structure; valid only for small disocclusions; requires temporal smoothing}
& \cite{Xu:2021:LGN,Pintore:2023:DSS} \\
Omnidirectional stereo
  & {Synthesize views on the eye's path, merge them into a pair of MCOP panoramas}
  & MCOP panorama
  & Stereo with head rotation
  & {Extremely compact, very fast rendering, but stereo-only; quality drops slightly in the peripheral areas and when the view direction converges towards the poles}
& \cite{Bourke:2010:COS,Pintore:2024:DSE} \\
Layered representation (MPI/MSI/MCI)
  & {Discretize scene into fronto-parallel (or spherical/cylindrical) layers; alpha compositing for parallax}
  & Discretized near-volumetric representation
  & Small–medium
  & {Low memory overhead only with few layers; quality degrades quickly far from original viewpoint unless many layers; good temporal continuity}
& \cite{Zhou:2018:SML,Tucker:2020:SVS,Attal:2020:MR6,Waidhofer:2022:PTL} \\
Explicit 3D model extraction
  & {Lift to explicit 3D model from single panorama via layout estimation and object recognition}
  & 3D model
  & Small–large
  & {Full renderable model with semantics available; works mostly in limited-vocabulary contexts}
& \cite{Xu:2017:P2C,Yang:2018:A3I,Zhang:2021:DPC,Dong:2024:PPT}\\
Neural representation from pseudo-multiviews
& {Hallucinate RGB-D images for novel viewpoints, merge them into a consistent 3D neural models}
  & Neural (3DGS, NeRF)
  & Small–large (method-dependent)
  & {Learned hallucination for disoccluded content mostly using foundation models; reconciling views proves hard}
  & \cite{Kulkarni:2023:3PN,Wang:2024:PPN,Pu:2024:PNV,Zhou:2024:HH3,Rey:2025:33P} \\
\hline
\end{tabular}
\endgroup
\caption{\textbf{Technique families used for \emph{novel-view synthesis from a single panorama}}. We separate approaches by representation and typical motion ranges, separating between small-to-medium (\autoref{sec:interactive:viewsynth:small}) and larger motions (\autoref{sec:interactive:viewsynth:large}).}
\label{tab:viewsynth_summary}
\end{table*}
%----------------------------------------------


%----------------------------------------------------------------------------------------------
\subsubsection{Handling limited disocclusions under small-to-medium viewpoint motion}
\label{sec:interactive:viewsynth:small}

A first class of approaches focuses on supporting restricted viewpoint changes, such as stereo generation or limited head motion. Stereo can be modeled as eye positions moving along a circular arc when the head rotates around the capture point, while small parallax corresponds to translations within a compact volume. Prior studies on novel-view synthesis~\cite{Serrano:2019:MP3} have reported that head displacements during interactive VR exploration generally remain within a range of approximately 35 cm. In such cases, specialized methods can be designed to manage modest disocclusions and perspective changes, enabling compact, fast-rendering representations tailored to the capture environment.

\paragraph*{Depth estimation and reprojection}
A general approach is to pair a panorama with an inferred view-independent depth map (\autoref{sec:geostruct:depth}) and render the resulting textured spherical meshes to achieve parallax~\cite{Zhao:2013:C2V,Huang:2017:6VV,Wei:2018:GVH,Tukur:2023:SFP}. These methods are efficient, since heavy computation is offline, but suffer from artifacts in disoccluded regions due to view-independent interpolation.

%------------------------------------------------------------
\begin{figure*}[htb]
    \centering
    {\includegraphics[width=0.9\linewidth]{img/redesigned-xu2021-layout-guided-viewsynth-360.jpg}}
    \caption{\textbf{Layout-guided view synthesis} The method introduced by Xu et al.~\cite{Xu:2021:LGN} shows the close connection between depth estimation, layout estimation, and novel view synthesis. }
    \label{fig:interactive:viewsynth:layout-guided-viewsynth360}
\end{figure*}
%------------------------------------------------------------

%------------------------------------------------------------
\begin{figure*}[htb]
    \centering
    {\includegraphics[width=0.9\linewidth]{img/redesigned-reyarea2024-3dgs-from-single360.jpg}}
    \caption{\textbf{Synthesis of a 3D Gaussian Splat representation from a single panorama}~\cite{Rey:2025:33P}. 
    Depth is first estimated from the input $360^\circ$ panorama to form an RGB-D representation, which is projected into multiple uniformly distributed perspective views to initialize a 3D Gaussian Splat scene. To enable limited head motion, novel nearby viewpoints are synthesized, disocclusions are inpainted, and the newly generated content is incorporated into the Gaussian representation through joint optimization with the original and synthesized views.
    }
    \label{fig:interactive:viewsynth:3dgs-from-single360}
\end{figure*}
%------------------------------------------------------------

\paragraph*{Indoor-specific inpainting and synthesis techniques}
Quality can be improved by rendering the depth image as a point cloud from the target view and using inpainting to fill disoccluded areas, as done in perspective images with classic~\cite{Sridevi:2019:IIB} and learning-based~\cite{Yu:2019:FII,Zeng:2019:LPE} methods. Structural priors and geometric constraints have proven useful for guiding novel color generation~\cite{Wiles:2020:SSE}. For indoor panoramas, end-to-end view synthesis networks exploiting domain-specific priors have been proposed to generate shifted views at runtime~\cite{Xu:2021:LGN,Pintore:2023:DSS}.  In these methods, a first offline stage extracts from the input panorama a dense feature map, depths, and room layout information (\autoref{sec:geostruct:layout}). In the second online stage, the information is reprojected via forward splatting and layout transformation, and the final complete view is produced from this partial view. \autoref{fig:interactive:viewsynth:layout-guided-viewsynth360} illustrates the pipeline of Xu et al.~\cite{Xu:2021:LGN}. Training uses synthetic databases (e.g., PNVS~\cite{Data:PNVS:2021,Xu:2021:LGN}) with source-target pairs. While enabling free-form motion, computational demands restrict the use of these methods on HMDs to remote rendering with upscaling~\cite{Pintore:2023:DSS}.

\paragraph*{Precomputed representations for small displacements}
To reduce per-frame synthesis costs, researchers have proposed a number of precomputed intermediate representations suitable for accelerating subsequent rendering. Classical methods were designed on multi-view input (e.g., Neural Reflectance Fields NeRF~\cite{Mildenhall:2021:NRS}, 3D Gaussian Splatting (3DGS)~\cite{Kerbl:2023:3GS}, depth- or flow-based proxies~\cite{Bertel:2019:MC3,Bertel:2020:OC3,Luo:2018:PS3,Huang:2017:6VV}), with 360$^\circ$-specific variants using layered images~\cite{Hedman:2017:C3P,Hedman:2018:I3P}, multi-depth panoramas~\cite{Lin:2020:DMD}, layered meshes~\cite{Broxton:2020:ILF}, or panoramic Gaussian splats~\cite{Bai:2025:3LG}. Recent work has adapted these multi-view representations to single-view input using synthesis approaches previously described to generate the required multi-view input. Multi-plane images (MPI)~\cite{Zhou:2018:SML}, later adapted to panoramas as multi-spherical (MSI)~\cite{Attal:2020:MR6} or multi-cylinder images (MCI)~\cite{Waidhofer:2022:PTL} are representations in which scenes are represented through geometrically simple slices taken at fixed distances. Their regular structure makes them suitable as a target for inference. Tucker and Snavely~\cite{Tucker:2020:SVS} introduced a method to infer an MPI from a single perspective image, and \emph{PanoSynthVR}~\cite{Waidhofer:2022:PTL} extended this approach to MCI representations. While effective for small motions, they blur at disocclusions and degrade rapidly with viewpoint shifts if the number of layers is maintained low, which is necessary to meet memory and rendering speed constraints. In their original presentation, no indoor-specific information is used for inference (e.g., layouts, as in indoor-specific view synthesis~\cite {Xu:2021:LGN,Pintore:2023:DSS}). Recently, interest has shifted from layered representations to neural representations. A prominent example is the 3603DPhotos~\cite{Rey:2025:33P} approach. Depth is first estimated from the input 360$^\circ$ panorama using methods from~\autoref{sec:geostruct:depth}. The resulting RGB-D panorama is then projected into 20 perspective RGB-D images, centered at the original capture position and uniformly distributed on the sphere. These images initialize a set of 3D Gaussian Splats, forming a first approximation of the 3D scene explaining the original 360$^\circ$ input. To support head movements, novel virtual views are sampled by shifting the origin within a small sphere (50 cm radius) from the original capture positions, and disoccluded regions in these views are inpainted with variants of the methods described earlier. The inpainted content is added as new Gaussians, and the representation is optimized to align with both the input and the inpainted images. After iterative refinement, the final result is a full implicit neural representation valid for all views within the defined head movement volume. The representation is reasonably fast to render and much more expressive than layered ones, but has a much higher memory occupation since, in 3DGS, each 3D Gaussian requires 48 parameters for view-dependent color representation via spherical harmonics and 7 parameters encoding anisotropic scale and rotation. A similar approach, for NeRFs rather than 3DGs, is provided by PERF~\cite{Wang:2024:PPN}, which is much more compact, but also much slower to render, especially on embedded devices. Recently introduced 3DGS compression solutions~\cite{Bagdasarian:2025:3S3} might be beneficial, but have not yet been adapted to 3DGSs created from single panoramas. 

%------------------------------------------------------------
\begin{figure*}[htb]
    \centering
    {\includegraphics[width=0.9\linewidth]{img/redesigned-pintore2024-ods-from-single360.jpg}}
    \caption{\textbf{Synthesis of omnidirectional stereo pairs from a single panorama} Slices generated by a view-synthesis network are composed in a single multiple-center-of-projection equirectangular image per eye~\cite{Pintore:2024:DSE}. }
    \label{fig:interactive:viewsynth:ods-from-single360}
\end{figure*}
%------------------------------------------------------------

\paragraph*{Precomputed representations for omnidirectional stereo}
Since stereo cues are especially critical indoors, due to the closeby objects, researchers have strived to create optimized representations for that case~\cite{Waidhofer:2022:PTL}. The solution space for novel view synthesis is constrained by the fact that during head rotations in HMDs, the eyes trace a small circular path around the capture point, with a radius of $\approx 10cm$. Each view can thus be parameterized as a function of the angle. PanoVerse~\cite{Pintore:2023:PAG} takes the straightforward approach of synthesizing a set of equally spaced views along the path. At display time, separately for each eye, the two images angularly closer to the current viewing direction are selected and cross-blended to provide a continuous experience. Storage constraints restrict the number of images, causing ghosting in transition regions. More compact solutions use omnidirectional stereo projection, a multiperspective approach~\cite{Rademacher:1998:MI,Peleg:1999:SPS} that encodes stereo in two equirectangular images with per-column centers of projection, displayed like conventional stereo panoramas~\cite{Bourke:2010:COS}. Pintore et al.~\cite{Pintore:2024:DSE} introduce a deep system that creates the representation by blending panoramic slices inferred from a single view with a custom view-synthesis network (\autoref{fig:interactive:viewsynth:ods-from-single360}). The method requires very limited rendering resources, but, as for all omnidirectional stereo methods~\cite {Marrinan:2021:ROS}, quality drops slightly in the peripheral areas and when the view direction converges towards the poles. Since depth is available, future work may leverage runtime, gaze-dependent adaptations~\cite{Marrinan:2021:ROS}.

%------------------------------------------------------------
\begin{figure*}[htb]
    \centering
    {\includegraphics[width=0.9\linewidth]{img/redesigned-pu2024-pano2room.jpg}}
    \caption{\textbf{Using geometric consistency for synthesizing a 3D Gaussian Splat representation from a single panorama}~\cite{Pu:2024:PNV}. With a panorama as input, augmented with measured or predicted depth, the method synthesizes an initial mesh. Next, it iteratively searches for a nearby camera pose with the lowest view completeness, reprojects the current mesh from the selected views, completes the panoramic view with an RGBD-D inpainter, and merges the new geometry with the current geometry, excluding portions that create conflicts. Finally, the inpainted mesh is converted to a 3DGS and trained with collected pseudo novel views.}
    \label{fig:interactive:viewsynth:pano2room}
\end{figure*}
%------------------------------------------------------------
%----------------------------------------------------------------------------------------------
\subsubsection{Novel content generation for large viewpoint displacements}
\label{sec:interactive:viewsynth:large}

When the user translates significantly from the capture location, the panorama must be lifted into a full 3D renderable model either by reconstructing a renderable geometric model using semantic priors or by synthesizing novel views with generative techniques and fusing them into a consistent model. 

\paragraph*{Layout reconstruction with object recognition} One strategy exploits semantic priors: methods such as Pano2CAD~\cite{Xu:2017:P2C}, Auto3DIndoor~\cite{Yang:2018:A3I}, DeepPanoContext~\cite{Zhang:2021:DPC}, and PanoContextFormer~\cite{Dong:2024:PPT} combine layout estimation with object recognition to reconstruct complete rooms. The general concept behind this class of objects is to apply variations of the methods presented in~\autoref{sec:geostruct:layout} to determine the architectural boundary of the room, and create a textured model of the emptied space. Then, individual objects recognized in the panoramic image are extracted and replaced with textured meshes. These approaches produce a very compelling representation, close to a building information model (BIM) with a structural, semantic, and visual model that can then be used for a variety of downstream applications, and not only for rendering. However, real environments often contain clutter and objects with unknown semantics, limiting reconstruction quality. For this reason, the methods tend not to work well in an open-vocabulary context~\cite{Pu:2024:PNV}.

\paragraph*{Neural 3D renderable representations from a single panorama} Learning-based renderable representations such as Neural Radiance Fields (NeRFs)~\cite{Mildenhall:2021:NRS} and 3D Gaussian Splatting (3DGS)~\cite{Kerbl:2023:3GS} offer an alternative to explicit shape and material models, producing photorealistic novel views but, in their original formulation, typically require many input images to optimize their parametric representation. Recent extensions address single-panorama input by synthesizing auxiliary, but possibly low-quality, views with variations of the methods described in previous sections, then training NeRF or 3DGS models to reconcile them. Examples include DietNeRF~\cite{Jain:2021:PND}, Pix2NeRF~\cite{Cai:2022:UCP}, SinNeRF~\cite{Xu:2022:STN}, NerfDiff~\cite{Gu:2023:NSV}, NerDi~\cite{Deng:2023:NSN}, 360FusionNerf~\cite{Kulkarni:2023:3PN}, PERF~\cite{Wang:2024:PPN}, PixelSplat~\cite{Charatan:2024:P3G},
HoloDreamer~\cite{Zhou:2024:HH3},
Pano2Room~\cite{Pu:2024:PNV}, 3603DPhotos~\cite{Rey:2025:33P}. State-of-the-art pipelines~\cite{Kulkarni:2023:3PN,Wang:2024:PPN,Pu:2024:PNV,Zhou:2024:HH3,Rey:2025:33P} typically generate RGB-D images for novel viewpoints and apply collaborative inpainting to fill missing regions before consolidating them into consistent 3D neural models. \autoref{fig:interactive:viewsynth:3dgs-from-single360} illustrates the concept behind the iterative creation of a 3D Gaussian Splat representation from a single panorama. While the method is mostly designed for small-to-medium motion ranges, it can also potentially be used for larger motion ranges by using pretrained generative models~\cite{Gokaslan:2024:COD} for the inpainting process~\cite{Kulkarni:2023:3PN,Rey:2025:33P}. However, sequential inpainting may constrain virtual camera trajectories and produce ghost geometries, while inconsistent novel views can introduce blurring. Pano2Room~\cite{Pu:2024:PNV} improves robustness under large motion ranges by first estimating a mesh, iteratively refining it with panoramic RGB-D inpainting, and finally converting the result into a 3DGS optimized with pseudo-novel views (\autoref{fig:interactive:viewsynth:pano2room}). Starting from a panorama augmented with measured or predicted depth, the method synthesizes an initial mesh. It then iteratively selects camera poses with the lowest view completeness, reprojects the current mesh from these views, completes the panoramas with an RGB-D inpainter, and merges the recovered geometry while discarding conflicting regions. The new, consistent geometry is used for the subsequent view planning and iterative improvement generation. The refined mesh is ultimately transformed into a 3DGS and trained using the collected novel pseudo views. Since inpainting is performed on reasonably large areas using a pre-trained generative diffusion model for standard images~\cite{Rombach:2022:HIS}, this strategy enables the insertion of novel content to support large motions, but also introduces challenges: diffusion-based inpainting may generate inconsistent geometry, and the incremental refinement loop can make maintaining global consistency difficult.

%----------------------------------------------------------------------------------------------
\subsubsection{Connecting multiple rooms}
\label{sec:interactive:viewsynth:multiroom}

Generative view-synthesis methods, such as those summarized in~\autoref{sec:interactive:viewsynth:large}, are, in principle, capable of handling arbitrary displacements and could be applied to multi-room navigation. For instance, \emph{360Roam}~\cite{Huang:2022:3RI} jointly extracts a floor plan and a NeRF representation of the environment from multi-view spherical input. The approach is, however, hard to apply to multi-room environments with just one image per room and little or no overlap. A full precomputation supporting arbitrary motion in such environments would be very costly to store and would be hard to generate while maintaining consistency everywhere. At the same time, generative methods have a high generation cost that hinders their run-time application to low-latency and high-frequency rendering in response to user motion. For this reason, the typical approach is to use them as building blocks for precomputing videos along paths that connect the original sampling locations.

The problem of generating smooth video transitions between panoramas has been extensively studied in the literature. Early approaches typically relied on detecting and matching feature points between the source and target panoramas, and then constraining the interpolation through warping combined with cross-blending~\cite{Zhao:2013:C2V}. While effective in simple cases, this strategy is prone to ghosting and structural deformations when the underlying geometries do not align well, an issue that becomes particularly severe for wide-baseline transitions, especially indoors, due to the presence of very nearby objects. To mitigate these artifacts, later work has integrated depth estimation and view synthesis, enabling more natural intermediate frames and thus perceptually smoother interpolations between panoramas (e.g.,~\cite{Li:2022:OS3,Chen:2023:PGS,Song:2025:NVS}). Still, these methods require a good amount of overlap between panoramas, which is often hard to obtain with just one panorama per room. Moreover, the confined nature of architectural spaces, the presence of occluding furniture, and the non-convexity of room layouts require more sophisticated trajectory design than simple generation of intermediate frames along linear paths. 

To address this, classical methods (e.g.,~\cite{DiBenedetto:2014:EEC}) have proposed computing curved transition paths that respect collision-avoidance constraints and smoothness objectives, with later refinements optimizing viewpoint selection for visual quality and user immersion~\cite{Feixas:2009:UIF,Boorboor:2023:SVS}. These techniques, however, typically assume access to detailed geometric models, resources that may not always be reliable in sparse capture settings. To address this, Pintore et al.~\cite{Pintore:2025:PRI} generate collision-free multi-room paths that enforce door traversal and clutter avoidance while minimizing the number of disoccluded pixels to preserve visual quality.

Building on purely visual input, a number of authors have explored the use of learned aesthetic metrics to guide viewpoint and trajectory selection. In particular, neural aesthetic predictors have been optimized directly on synthesized views~\cite{Wei:2018:GVH}, allowing systems to automatically identify visually pleasing camera positions~\cite{Alzayer:2021:AAP,Uchida:2025:3VO} and to plan navigation paths that maximize aesthetic quality~\cite{Xie:2023:GGA}. These methods represent an important step toward integrating perceptual considerations into navigation design, but they are typically restricted to relatively simple scenarios, such as navigation within a single room. As a result, they do not address the structural complexity and occlusion challenges that arise in multi-room indoor environments. 

%===========================================================================================
\subsection{Dynamic modifications}
\label{sec:interactive:editing}

The components surveyed in \autoref{sec:geostruct} deliver pixel-wise depth/normal/semantic estimates, room layout abstractions, and graph connectivity across spaces. These reconstructions are not only useful for navigation (\autoref{sec:interactive:viewsynth}), but they are essential to support editing with limited or no user input, as geometry proxies guide occlusion reasoning and re-projection, semantics provide category-level control, and coarse illumination estimates support photometric consistency. We discuss three common editing tasks enabled by this panorama-first pipeline:
\begin{enumerate}
\item \emph{Diminished reality/clutter removal} aims to remove objects or categories (e.g., furniture) and plausibly synthesize the newly revealed background at the original viewpoint.  
\item \emph{Virtual staging \& inverse rendering} insert, rearrange, or restyle furniture while maintaining physical plausibility by estimating intrinsic scene factors (geometry, reflectance, and illumination).  
\item \emph{Style transfer} re-styles the overall scene (appearance, materials, or tone) while preserving the spherical geometry and semantics of the panorama.
\end{enumerate}
\paragraph*{Diminished reality and emptying}
Early panorama-specific works demonstrated that category-aware removal combined with layout priors can \emph{empty} cluttered rooms with minimal supervision. Pintore \emph{et~al.} perform instant automatic emptying by fusing semantics with layout-consistent inpainting and geometry-aware view constraints, producing background completions that respect walls, floors, and large structural boundaries \cite{Pintore:2022:IAE}. Complementarily, \emph{PanoDR} targets spherical diminished reality for indoor scenes by operating directly in equirectangular space and enforcing spherical consistency during removal and completion \cite{Gkitsas:2021:PSP}. More recently, Slavcheva et al.~\cite{Slavcheva:2024:ADIP} exploited diffusion models for automatic defurnishing. Dolhasz et al.~\cite{Dolhasz:2025:defurnishing} extended this approach by integrating a mesh representation to enhance the controllability of the defurnishing diffusion model. 
These approaches illustrate a key theme of this survey: the same reconstruction signals that make navigation robust (depth, normals, layout) also reduce ambiguity during removal and completion, allowing largely automatic pipelines with only coarse category selection (or none at all).

\paragraph*{Virtual staging via inverse rendering and object insertion}
Virtual staging benefits from disentangling appearance into geometry, reflectance, and light so that inserted assets “sit” correctly, cast/shadow realistically, and respond to the scene illumination. In practice, inverse rendering from \emph{one} $360^{\circ}$ image is severely under-constrained: to explain the observed radiance, the system must \emph{jointly} estimate multiple dense signals—geometry (depth, normals), intrinsic layers (albedo and shading), material/BRDF cues, semantics, and HDR illumination—while remaining consistent with spherical projection. 
%
Multi-task dense-prediction frameworks designed for panoramas address this need by producing several of these cues in one pass~\cite{Zhi:2022:SSAD,Li:2022:PPI,Shah:2024:MHD}. Such multi-signal estimates stabilize contact and occlusion reasoning, improve shadow and reflection plausibility, and supply strong priors for subsequent optimization.
Exploiting these ideas, SSAD learns a semantically supervised appearance decomposition from a \emph{single} panorama, enabling realistic staging without multi-view capture \cite{Zhi:2022:SSAD}. 
\emph{PhyIR} brings explicit physics into the loop, recovering scene intrinsics and environment illumination for panoramic indoor images, which in turn improves relighting and consistent object shading \cite{Li:2022:PPI}. 
Finally, \emph{VISPI} integrates multi-task learning (geometry/semantics/illumination)~\cite{Shah:2024:MHD} with inverse rendering to support turnkey virtual staging from one panorama, including insertion and material harmonization, and contact reasoning \cite{Shah:2025:VISPI}.  
Recent diffusion-based approaches further boost photorealism and reduce user effort by coupling generative priors with physically grounded factors~\cite{Pu:2024:PNV}. Diffusion-guided inverse rendering optimizes geometry/BRDF/lighting to match a powerful image prior, enabling high-fidelity object insertion with correct material response and shadows \cite{Liang:2025:POID}. 
Latent-intrinsic formulations jointly reason about intrinsics and appearance in a diffusion backbone to achieve robust indoor relighting and material edits \cite{Xing:2025:luminet}, while diffusion-driven single-image illumination estimation produces HDR environment lighting suitable for staging and re-rendering \cite{Shen:2025:IllumiDiff}. 
Operating directly in an intrinsic-image latent space, \emph{IntrinsicEdit} provides precise, local manipulations (materials, global relighting, insertion/removal) with improved identity preservation and consistent global illumination \cite{Lyu:2025:IntrinsicEdit}. 
Across these methods, the proxies from \autoref{sec:geostruct:depth} and \autoref{sec:geostruct:layout} (depth, normals, floor/wall estimates) are pivotal for contact estimation, collision checking, and occlusion ordering, while \autoref{sec:geostruct:connect} adds spatial context for multi-room staging scenarios.

\paragraph*{Panoramic style transfer}
Panoramic style transfer must respect spherical distortions and large field-of-view coherence. \emph{PanoStyle} couples semantic and geometry cues to achieve shading-independent photorealistic transfer in indoor panoramas, preserving structural boundaries and material identity \cite{Tukur:2023:PSG,Tukur:2025:PSS}. 
\emph{PAST} introduces deformable distortion constraints tailored to panoramas, improving the alignment of stylistic features over long geodesics \cite{Ye:2025:PAST}. 
When multiple panoramas are available (e.g., per-room captures connected in \autoref{sec:interactive:viewsynth:multiroom}), \emph{PIST} targets cross-view consistency via multi-scale attention and global feature sharing to avoid view-to-view style drift \cite{wang:2025:PIST}. Together, these techniques complement staging by harmonizing inserted assets with existing decor and enabling global appearance changes without full re-rendering.

\paragraph*{Discussion}
Diminished reality, virtual staging with inverse rendering, and style transfer are three faces of the same capability: \emph{editability from a single capture}. The reconstruction signals established earlier provide the geometric and photometric scaffolding that turns one-shot panoramas into editable scenes. As a result, modern systems increasingly offer end-to-end, low-touch workflows—\emph{empty} first to declutter, \emph{stage} with inverse-rendered consistency, and optionally \emph{re-style}—all while remaining compatible with the interactive view-synthesis tools of \autoref{sec:interactive:viewsynth}. 
Concurrently, latest research trends increasingly aim to generate \emph{entire 3D scenes} from scratch or with minimal user input, such as a textual prompt. In the context of \emph{panoramic} scene generation, the last year has seen a surge of diffusion-based methods that operate directly in the spherical domain or in equirectangular projection, producing $360^{\circ}$ RGB images (and sometimes approximate geometry/semantics) from text prompts. Representative examples include spherical/equirectangular text-to-360 diffusion models \cite{Zhou:2024:DS360,Ye:2024:DiffPano}, controllable room synthesis that accepts layout or category constraints \cite{Fang:2025:Ctrl-Room,Zhang:2024:TSD}, layered or geometry-aware generators that target 3D-consistent outputs from a single panorama \cite{Yang:2025:LayerPano3D}, and systems or datasets that scale to multi-room and world-like environments \cite{Huang:2025:Scene4U,Yu:2025:Wonderworld}. While training objectives and architectural details vary, these approaches share a goal: \emph{photorealistic, prompt-aligned 360 content with enough structural coherence to support downstream use.}

