State-of-the-art in deep learning approaches for automatic single-panorama indoor modeling and exploration 
- Giovanni Pintore CRS4, Italy
- Marco Agus HBKU, Qatar
- Jens Schneider HBKU, Qatar
- Enrico Gobbetti CRS4, Italy
A single surround-view panoramic image provides complete coverage of the environment visible from a single viewpoint and inherently supports dynamic exploration, especially when viewed through a head-mounted display. For these reasons, single or linked 360-degree panoramas have become a widely adopted modality for indoor scene acquisition and virtual tour creation. Despite their popularity, panoramas present inherent limitations: they statically represent the captured scene, do not provide an explicit 3D architectural structure or geometry, and exhibit minimal parallax due to their single viewpoint. These limitations restrict their applicability and often require significant modeling efforts to generate missing data. In our survey, we provided an up-to-date, integrative overview of recent techniques to address these challenges, bringing together complementary perspectives from machine learning, computer vision, and computer graphics.
Note: This repository accompanies our survey published in Computer Graphics Forum and presented at Eurographics 2026. It provides direct links to relevant works, organized by topic. We will strive to keep this repository updated also after the survey publication.
If you find this survey or the organized repository helpful for your research, please cite our work as follows:
APA:
Pintore, G., Agus, M., Schneider, J., & Gobbetti, E. (2026). State-of-the-art in deep learning approaches for automatic single-panorama indoor modeling and exploration. Computer Graphics Forum, 45(2), doi: 10.1111/cgf.70396
BibTeX:
@article{Pintore:2026:SDL,
title={State-of-the-art in deep learning approaches for automatic single-panorama indoor modeling and exploration},
author={Giovanni Pintore and Marco Agus and Jens Schneider and Enrico Gobbetti},
journal={Computer Graphics Forum},
volume={45},
number={2},
year={2026},
doi={10.1111/cgf.70396}
}This work was supported by NPRP-S 14th Cycle grant 0403-210132 AIN2 from the Qatar National Research Fund (a member of Qatar Foundation) and by the Sardinian Regional Authorities under the project XDATA (Art9 LR 20/2015).
- Related surveys
- Background
- Automatic geometric and structural modeling
- Novel view synthesis and immersive model exploration
- Emerging pre-trained and foundation models
- Applications
Our 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 asingle panoramic image, covering both analysis (for model reconstruction) and synthesis (for immersive viewing, navigation, and editing). Several other surveys provide complementary information. We list here relevant works.
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In addition to reviewing state-of-the-art methods, we outline key background concepts, including omnidirectional representations, gravity alignment, architectural priors, and major datasets supporting 360-degree indoor research.
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References:
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Single 360 Image Beyond the Manhattan World
Assumption," Proc. ECCV, 2020, doi: 10.1007/978-3-030-58598-3_26.
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Structured Indoor Environments," Comput. Graph. Forum, 2020, doi: 10.1111/cgf.14021.
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These methods infer geometric, structural, and visual properties of indoor scenes from a single omnidirectional panorama (or approximately one per room in multi-room settings). Approaches range from pixel-level augmentation (e.g., depth, normals) to higher-level structural abstractions of room layouts and objects, and extend to sparse multi-room reasoning that recovers layouts and inter-room connectivity for navigation and exploration.
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Single-panorama indoor inference is highly ill-posed and relies on strong priors typically learned from large annotated datasets, whose creation and use are costly and often task-specific. Foundation models trained on large-scale data are emerging as a possible alternative by enabling reusable representations that can be adapted to many tasks. In 360-degree analysis, they are mainly used either by repurposing standard vision models for omnidirectional problems or by developing models specifically designed for panoramic imagery.
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Panorama-first workflows are enabling practical applications across domains such as AEC/BIM documentation, education and safety training, cultural heritage dissemination, remote collaboration, and real estate marketing, where plausible and easily captured models are often more valuable than precise measurements.
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