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  title =         {{ExploreMaps}: Efficient Construction and Ubiquitous
                   Exploration of Panoramic View Graphs of Complex {3D}
                   Environments},
  volume =        {33},
  year =          {2014},
  doi =           {10.1111/cgf.12334},
}

@article{Song:2025:NVS,
  author =        {Song, Kai and Zhang, Lei},
  journal =       {Computers \& Graphics},
  pages =         {104139},
  title =         {Novel view synthesis with wide-baseline stereo pairs
                   based on local--global information},
  volume =        {126},
  year =          {2025},
  doi =           {10.1016/j.cag.2024.104139},
}

@article{Pintore:2018:R3E,
  author =        {Giovanni Pintore and Ruggero Pintus and
                   Fabio Ganovelli and Roberto Scopigno and
                   Enrico Gobbetti},
  journal =       {Computers \& Graphics},
  pages =         {16--29},
  title =         {Recovering {3D} existing-conditions of indoor
                   structures from spherical images},
  volume =        {77},
  year =          {2018},
  doi =           {10.1016/j.cag.2018.09.013},
}

@inproceedings{Yan:2025:SEP,
  author =        {Yan, Qingsong and Wang, Qiang and Zhao, Kaiyong and
                   Chen, Jie and Li, Bo and Chu, Xiaoweo and Deng, Fei},
  booktitle =     {Proc. 3DV},
  organization =  {},
  pages =         {855--865},
  title =         {SphereFusion: Efficient Panorama Depth Estimation via
                   Gated Fusion},
  year =          {2025},
  doi =           {10.1109/3DV66043.2025.00084},
}

@article{Huang:2025:GGP,
  author =        {Huang, Chengchao and Shao, Feng and Chen, Hangwei and
                   Mu, Baoyang and Xu, Long},
  journal =       {IEEE Trans. Circuits and Systems for Video
                   Technology},
  number =        {9},
  pages =         {9060-9074},
  title =         {GADFNet: Geometric Priors Assisted Dual-Projection
                   Fusion Network for Monocular Panoramic Depth
                   Estimation},
  volume =        {35},
  year =          {2025},
  doi =           {10.1109/TCSVT.2025.3553472},
}

@article{Wang:2025:GSI,
  author =        {Wang, Xu and He, Ziyan and Zhang, Qiudan and
                   Yang, You and Zhao, Tiesong and Jiang, Jianmin},
  journal =       {IEEE Trans. Multimedia},
  number =        {},
  pages =         {3224-3237},
  title =         {Geometry-Aware Self-Supervised Indoor 360°
                   Depth Estimation via Asymmetric Dual-Domain
                   Collaborative Learning},
  volume =        {27},
  year =          {2025},
  doi =           {10.1109/TMM.2025.3535340},
}

@article{Meng:2025:ISG,
  author =        {Meng, Ming and Zhu, Yonggui and Zhao, Yufei and
                   Li, Zhaoxin and Zhu, Zhe},
  journal =       {Computational Visual Media},
  number =        {3},
  pages =         {431-464},
  title =         {{3D} Indoor Scene Geometry Estimation from a Single
                   Omnidirectional Image: A Comprehensive Survey},
  volume =        {11},
  year =          {2025},
  doi =           {10.26599/CVM.2025.9450438},
}

@article{Ai:2025:SRL,
  author =        {Hao Ai and Zidong Cao and Lin Wang},
  journal =       {Int J Comput Vis},
  number =        {},
  pages =         {4973–5012},
  title =         {A Survey of Representation Learning, Optimization
                   Strategies, and Applications for Omnidirectional
                   Vision},
  volume =        {133},
  year =          {2025},
  doi =           {10.1007/s11263-025-02391-w},
}

@inproceedings{Wang:2018:SSL,
  author =        {Wang, Fu-En and Hu, Hou-Ning and Cheng, Hsien-Tzu and
                   Lin, Juan-Ting and Yang, Shang-Ta and Shih, Meng-Li and
                   Chu, Hung-Kuo and Sun, Min},
  booktitle =     {Proc. ACCV},
  pages =         {53--68},
  title =         {Self-supervised learning of depth and camera motion
                   from 360 videos},
  year =          {2018},
  doi =           {10.1007/978-3-030-20873-8_4},
}

@inproceedings{Tran:2021:SSI,
  author =        {Tran, Phi Vu},
  booktitle =     {Proc. CVPR},
  pages =         {15353--15362},
  title =         {{SSLayout360}: Semi-supervised indoor layout
                   estimation from 360deg panorama},
  year =          {2021},
  doi =           {10.1109/CVPR46437.2021.01510},
}

@inproceedings{Yu:2023:PU3,
  author =        {Yu, Haozheng and He, Lu and Jian, Bing and
                   Feng, Weiwei and Liu, Shan},
  booktitle =     {Proc. CVPR},
  pages =         {878--887},
  title =         {{PanelNet}: Understanding 360 indoor environment via
                   panel representation},
  year =          {2023},
  doi =           {10.1109/CVPR52729.2023.00091},
}

@inproceedings{Pintore:2025:NRM,
  author =        {Pintore, Giovanni and Shah, Uzair and Agus, Marco and
                   Gobbetti, Enrico},
  booktitle =     {Proc. CVPR},
  pages =         {1985--1994},
  title =         {{NadirFloorNet}: reconstructing multi-room floorplans
                   from a small set of registered panoramic images},
  year =          {2025},
  doi =           {10.1109/CVPRW67362.2025.00186},
}

@inproceedings{Yu:2023:ADL,
  author =        {Yu, Jingrui and Grassi, Ana Cecilia Perez and
                   Hirtz, Gangolf},
  booktitle =     {Proc. CVPRW},
  number =        {},
  pages =         {6421-6433},
  title =         {Applications of Deep Learning for Top-View
                   Omnidirectional Imaging: A Survey},
  volume =        {},
  year =          {2023},
  doi =           {10.1109/CVPRW59228.2023.00683},
}

@inproceedings{Tsai:2024:NMA,
  author =        {Tsai, Yu-Ju and Jhang, Jin-Cheng and Zheng, Jingjing and
                   Wang, Wei and Chen, Albert YC and Sun, Min and
                   Kuo, Cheng-Hao and Yang, Ming-Hsuan},
  booktitle =     {Proc. CVPR},
  pages =         {28056--28065},
  title =         {No more ambiguity in 360° room layout via
                   bi-layout estimation},
  year =          {2024},
  doi =           {10.1109/CVPR52733.2024.02650},
}

@inproceedings{Sun:2024:SD2,
  author =        {Sun, Cheng and Tai, Wei-En and Shih, Yu-Lin and
                   Chen, Kuan-Wei and Syu, Yong-Jing and
                   Wang, Yu-Chiang Frank and Chen, Hwann-Tzong},
  booktitle =     {Proc. CVPR},
  pages =         {10435--10445},
  title =         {{Seg2Reg}: Differentiable {2D} Segmentation to {1D}
                   Regression Rendering for 360° Room Layout
                   Reconstruction},
  year =          {2024},
  doi =           {10.1109/CVPR52733.2024.00993},
}

@inproceedings{Lee:2025:HHP,
  author =        {Lee, Jongsung and Park, Harin and Lee, Byeong-Uk and
                   Joo, Kyungdon},
  booktitle =     {Proc. CVPR},
  pages =         {16599-16608},
  title =         {{HUSH}: Holistic Panoramic {3D} Scene Understanding
                   using Spherical Harmonics},
  year =          {2025},
  doi =           {10.1109/CVPR52734.2025.01547},
}

@inproceedings{Song:2016:SSC,
  author =        {Song, Shuran and Xiao, Jianxiong},
  booktitle =     {Proc. CVPR},
  pages =         {808--816},
  title =         {Deep Sliding Shapes for Amodal {3D} Object Detection
                   in {RGB-D} Images},
  year =          {2016},
  doi =           {10.1109/CVPR.2016.94},
}

@inproceedings{Qi:2018:FRN,
  author =        {Qi, Charles R. and Liu, Wei and Wu, Chenxia and
                   Su, Hao and Guibas, Leonidas J.},
  booktitle =     {Proc. CVPR},
  pages =         {918--927},
  title =         {Frustum PointNets for {3D} Object Detection from
                   {RGB-D} Data},
  year =          {2018},
  doi =           {10.1109/CVPR.2018.00102},
}

@article{Ren:2015:FRT,
  author =        {Ren, Shaoqing and He, Kaiming and Girshick, Ross and
                   Sun, Jian},
  journal =       {IEEE Transactions on Pattern Analysis and Machine
                   Intelligence},
  number =        {6},
  pages =         {1137-1149},
  title =         {Faster R-CNN: Towards Real-Time Object Detection with
                   Region Proposal Networks},
  volume =        {39},
  year =          {2017},
  doi =           {10.1109/TPAMI.2016.2577031},
}

@inproceedings{Chang:2015:SNI,
  author =        {Chang, Angel X. and Funkhouser, Thomas and
                   Guibas, Leonidas and Hanrahan, Pat and Huang, Qixing and
                   Li, Zimo and Savarese, Silvio and Savva, Manolis and
                   Song, Shuran and Su, Hao and Xiao, Jianxiong and
                   Yi, Li and Yu, Fisher},
  booktitle =     {arXiv preprint arXiv:1512.03012},
  title =         {{ShapeNet}: An Information-Rich {3D} Model
                   Repository},
  year =          {2015},
  doi =           {10.48550/arXiv.1512.03012},
}

@inproceedings{Liu:2022:THF,
  author =        {Liu, Haolin and Zheng, Yujian and Chen, Guanying and
                   Cui, Shuguang and Han, Xiaoguang},
  booktitle =     {Proc. ECCV},
  pages =         {429--446},
  title =         {Towards High-Fidelity Single-View Holistic
                   Reconstruction of Indoor Scenes},
  year =          {2022},
  doi =           {10.1007/978-3-031-19769-7_25},
}

@article{Tulsiani:2018:MP3D,
  author =        {Tulsiani, Shubham and Zhou, Tinghui and
                   Efros, Alexei A. and Malik, Jitendra},
  journal =       {IEEE Transactions on Pattern Analysis and Machine
                   Intelligence},
  number =        {12},
  pages =         {8754-8765},
  title =         {Multi-View Supervision for Single-View Reconstruction
                   via Differentiable Ray Consistency},
  volume =        {44},
  year =          {2022},
  doi =           {10.1109/TPAMI.2019.2898859},
}

@inproceedings{Mescheder:2019:ONL,
  author =        {Mescheder, Lars and Oechsle, Michael and
                   Niemeyer, Michael and Nowozin, Sebastian and
                   Geiger, Andreas},
  booktitle =     {Proc. CVPR},
  pages =         {4460--4470},
  title =         {Occupancy Networks: Learning {3D} Reconstruction in
                   Function Space},
  year =          {2019},
  doi =           {10.1109/CVPR.2019.00459},
}

@inproceedings{Sitzmann:2020:IFR,
  author =        {Sitzmann, Vincent and Martel, Julien and
                   Bergman, Alexander and Lindell, David and
                   Wetzstein, Gordon},
  booktitle =     {Proc. NeurIPS},
  title =         {Implicit Neural Representations with Periodic
                   Activation Functions},
  year =          {2020},
  doi =           {10.48550/arXiv.2006.09661},
}

@inproceedings{Huang:2018:CHS,
  author =        {Huang, Siyuan and Qi, Siyuan and Zhu, Yixin},
  booktitle =     {Proc. NeurIPS},
  pages =         {294--305},
  title =         {Cooperative Holistic Scene Understanding: Unifying
                   {3D} Object, Layout, and Camera Pose Estimation},
  year =          {2018},
  url =           {https://dl.acm.org/doi/abs/10.5555/3326943.3326963},
}

@inproceedings{Nie:2020:T3U,
  author =        {Nie, Yinyu and Hou, Ji and Han, Xiaoguang and
                   Nießner, Matthias},
  booktitle =     {Proc. CVPR},
  pages =         {55--64},
  title =         {Total3DUnderstanding: Joint Layout, Object Pose and
                   Mesh Reconstruction for Indoor Scenes from a Single
                   Image},
  year =          {2020},
  doi =           {10.1109/CVPR42600.2020.00013},
}

@inproceedings{Zhang:2014:PC,
  author =        {Zhang, Yinda and Song, Shuran and Tan, Ping and
                   Xiao, Jianxiong},
  booktitle =     {Proc. ECCV},
  pages =         {668--686},
  title =         {{PanoContext}: A Whole-room {3D} Context Model for
                   Panoramic Scene Understanding},
  year =          {2014},
}

@inproceedings{Liu:2021:STH,
  author =        {Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and
                   Wei, Yixuan and Zhang, Zheng and Lin, Stephen and
                   Guo, Baining},
  booktitle =     {Proc. ICCV},
  pages =         {10012--10022},
  title =         {Swin Transformer: Hierarchical Vision Transformer
                   using Shifted Windows},
  year =          {2021},
  doi =           {10.1109/ICCV48922.2021.00986},
}

@inproceedings{Touvron:2021:TDI,
  author =        {Touvron, Hugo and Cord, Matthieu and Douze, Matthijs and
                   Massa, Francisco and Sablayrolles, Alexandre and
                   Jégou, Hervé},
  booktitle =     {Proc. ICML},
  title =         {Training Data-efficient Image Transformers \&
                   Distillation through Attention},
  year =          {2021},
  url =           {https://proceedings.mlr.press/v139/touvron21a},
}

@inproceedings{Radford:2021:LTV,
  author =        {Radford, Alec and Kim, Jong Wook and Hallacy, Chris and
                   Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and
                   Sastry, Girish and Askell, Amanda and Mishkin, Pamela and
                   Clark, Jack and Krueger, Gretchen and
                   Sutskever, Ilya},
  booktitle =     {Proc. ICML},
  title =         {Learning Transferable Visual Models From Natural
                   Language Supervision},
  year =          {2021},
  url =           {https://icml.cc/virtual/2021/oral/9194},
}

@inproceedings{Hu:2021:MMT,
  author =        {Hu, Ronghang and Singh, Amanpreet},
  booktitle =     {Proc. ICML},
  title =         {Uniting Vision-and-Language Tasks via Text
                   Generation},
  year =          {2021},
  doi =           {10.48550/arXiv.2102.02779},
}

@inproceedings{Liu:2021:G3O,
  author =        {Liu, Zechen and Zhang, Zizhang and Cao, Yue and
                   Hu, Han and Tong, Xin and Dai, Bo and Lin, Stephen},
  booktitle =     {Proc. ICCV},
  pages =         {2949--2958},
  title =         {Group-Free {3D} Object Detection via Transformers},
  year =          {2021},
  doi =           {10.1109/ICCV48922.2021.00294},
}

@inproceedings{Schult:2023:MT3,
  author =        {Schult, Jonas and Engelmann, Francis and
                   Kontogianni, Theodora and Leibe, Bastian},
  booktitle =     {Proc. ICCV},
  title =         {{Mask3D}: Mask Transformer for {3D} Semantic Instance
                   Segmentation},
  year =          {2023},
  doi =           {10.1109/ICRA48891.2023.10160590},
}

@inproceedings{Chen:2022:PJP,
  author =        {Chen, Xiaoxue and Zhao, Hao and Zhou, Guyue and
                   Zhang, Ya-Qin},
  booktitle =     {Proc. CVPR},
  title =         {{PQ-Transformer}: Jointly Parsing {3D} Objects and
                   Layouts from Point Clouds},
  year =          {2022},
  doi =           {10.1109/LRA.2022.3143224},
}

@inproceedings{Dong:2023:SAG,
  author =        {Dong, Mingyue and Huan, Linxi and Xiong, Hanjiang and
                   Shen, Shuhan and Zheng, Xianwei},
  booktitle =     {Proc. ICCV},
  pages =         {21916--21926},
  title =         {Shape anchor guided holistic indoor scene
                   understanding},
  year =          {2023},
  doi =           {10.1109/ICCV51070.2023.02003},
}

@inproceedings{Wang:2022:BTF,
  author =        {Wang, Yikai and Ye, TengQi and Cao, Lele and
                   Huang, Wenbing and Sun, Fuchun and He, Fengxiang and
                   Tao, Dacheng},
  booktitle =     {Proc. CVPR},
  pages =         {12114--12123},
  title =         {Bridged transformer for vision and point cloud {3D}
                   object detection},
  year =          {2022},
  doi =           {10.1109/CVPR52688.2022.01180},
}

@inproceedings{Li:2025:BBA,
  author =        {Li, Yuguang and Boyadzhiev, Ivaylo and Liu, Zixuan and
                   Shapiro, Linda and Colburn, Alex},
  booktitle =     {Proc. CVPR},
  pages =         {16785-16795},
  title =         {BADGR: Bundle Adjustment Diffusion Conditioned by
                   Gradients for Wide-Baseline Floor Plan
                   Reconstruction},
  year =          {2025},
  doi =           {10.1109/CVPR52734.2025.01564},
}

@inproceedings{Lambert:2022:SSA,
  author =        {Lambert, John and Li, Yuguang and Boyadzhiev, Ivaylo and
                   Wixson, Lambert and Narayana, Manjunath and
                   Hutchcroft, Will and Hays, James and Dellaert, Frank and
                   Kang, Sing Bing},
  booktitle =     {Proc. ECCV},
  organization =  {Springer},
  pages =         {647--664},
  title =         {Salve: Semantic alignment verification for floorplan
                   reconstruction from sparse panoramas},
  year =          {2022},
  doi =           {10.1007/978-3-031-19821-2_37},
}

@article{Raffel:2020:ELT,
  author =        {Raffel, Colin and Shazeer, Noam and Roberts, Adam and
                   Lee, Katherine and Narang, Sharan and Matena, Michael and
                   Zhou, Yanqi and Li, Wei and Liu, Peter J.},
  journal =       {J. Mach. Learn. Res.},
  number =        {1},
  publisher =     {JMLR.org},
  title =         {Exploring the limits of transfer learning with a
                   unified text-to-text transformer},
  volume =        {21},
  year =          {2020},
  abstract =      {Transfer learning, where a model is first pre-trained
                   on a data-rich task before being fine-tuned on a
                   downstream task, has emerged as a powerful technique
                   in natural language processing (NLP). The
                   effectiveness of transfer learning has given rise to
                   a diversity of approaches, methodology, and practice.
                   In this paper, we explore the landscape of transfer
                   learning techniques for NLP by introducing a unified
                   framework that converts all text-based language
                   problems into a text-to-text format. Our systematic
                   study compares pretraining objectives, architectures,
                   unlabeled data sets, transfer approaches, and other
                   factors on dozens of language understanding tasks. By
                   combining the insights from our exploration with
                   scale and our new "Colossal Clean Crawled Corpus", we
                   achieve state-of-the-art results on many benchmarks
                   covering summarization, question answering, text
                   classification, and more. To facilitate future work
                   on transfer learning for NLP, we release our data
                   set, pre-trained models, and code.},
  issn =          {1532-4435},
}

@inproceedings{Li:2026:DDA,
  author =        {Li, Haodong and Zheng, Wangguangdong and He, Jing and
                   Liu, Yuhao and Lin, Xin and Yang, Xin and
                   Chen, Ying-Cong and Guo, Chunchao},
  booktitle =     {Proc. ICLR},
  title =         {{DA$^{2}$}: {Depth Anything} in Any Direction},
  year =          {2026},
  url =           {https://depth-any-in-any-dir.github.io/},
}

@article{Jiang:2025:DA3,
  author =        {Jiang, Hualie and Song, Ziyang and Lou, Zhiqiang and
                   Xu, Rui and Tan, Minglang},
  journal =       {arXiv preprint arXiv:2512.22819},
  title =         {{Depth Anything} in 360°: Towards Scale
                   Invariance in the Wild},
  year =          {2025},
  doi =           {10.48550/arXiv.2512.22819},
}

@inproceedings{Guo:2025:DAC,
  author =        {Guo, Yuliang and Garg, Sparsh and
                   Miangoleh, S Mahdi H and Huang, Xinyu and Ren, Liu},
  booktitle =     {Proc. CVPR},
  pages =         {26996--27006},
  title =         {{Depth Any Camera}: Zero-shot metric depth estimation
                   from any camera},
  year =          {2025},
  doi =           {10.1109/CVPR52734.2025.02514},
}

@inproceedings{Zhou:2024:DS360,
  author =        {Zhou, Shijie and Fan, Zhiwen and Xu, Dejia and
                   Chang, Haoran and Chari, Pradyumna and
                   Bharadwaj, Tejas and You, Suya and Wang, Zhangyang and
                   Kadambi, Achuta},
  booktitle =     {Proc. ECCV},
  pages =         {324--342},
  title =         {{DreamScene360}: Unconstrained Text-to-3D Scene
                   Generation with Panoramic Gaussian Splatting},
  year =          {2025},
  doi =           {10.1007/978-3-031-72658-3_19},
  isbn =          {978-3-031-72658-3},
}

@inproceedings{Ye:2024:DiffPano,
  author =        {Ye, Weicai and Ji, Chenhao and Chen, Zheng and
                   Gao, Junyao and Huang, Xiaoshui and Zhang, Song-Hai and
                   Ouyang, Wanli and He, Tong and Zhao, Cairong and
                   Zhang, Guofeng},
  booktitle =     {Proc. NeurIPS},
  pages =         {1304--1332},
  title =         {{DiffPano}: Scalable and Consistent Text to Panorama
                   Generation with Spherical Epipolar-Aware Diffusion},
  volume =        {37},
  year =          {2024},
  doi =           {10.48550/arXiv.2410.24203},
}

@inproceedings{Fang:2025:Ctrl-Room,
  author =        {Fang, Chuan and Dong, Yuan and Luo, Kunming and
                   Hu, Xiaotao and Shrestha, Rakesh and Tan, Ping},
  booktitle =     {Proc. 3DV},
  number =        {},
  pages =         {692-701},
  title =         {{Ctrl-Room}: Controllable {Text-to-3D} Room Meshes
                   Generation with Layout Constraints},
  volume =        {},
  year =          {2025},
  doi =           {10.1109/3DV66043.2025.00069},
}

@article{Lewis:2019:BDS,
  author =        {Lewis, Mike and Liu, Yinhan and Goyal, Naman and
                   Ghazvininejad, Marjan and Mohamed, Abdelrahman and
                   Levy, Omer and Stoyanov, Ves and Zettlemoyer, Luke},
  journal =       {arXiv preprint arXiv:1910.13461},
  title =         {{BART}: Denoising sequence-to-sequence pre-training
                   for natural language generation, translation, and
                   comprehension},
  year =          {2019},
  doi =           {10.48550/arXiv.1910.13461},
}

@inproceedings{Zhang:2024:TSD,
  author =        {Zhang, Cheng and Wu, Qianyi and
                   Gambardella, Camilo Cruz and Huang, Xiaoshui and
                   Phung, Dinh and Ouyang, Wanli and Cai, Jianfei},
  booktitle =     {Proc. CVPR},
  number =        {},
  pages =         {6347-6357},
  title =         {Taming Stable Diffusion for Text to 360°
                   Panorama Image Generation},
  volume =        {},
  year =          {2024},
  doi =           {10.1109/CVPR52733.2024.00607},
}

@article{Huang:2022:3RI,
  author =        {Huang, Huajian and Chen, Yingshu and Zhang, Tianjia and
                   Yeung, Sai-Kit},
  journal =       {arXiv preprint arXiv:2208.02705},
  title =         {{360Roam}: Real-Time Indoor Roaming Using
                   Geometry-Aware 360° Radiance Fields},
  year =          {2022},
  doi =           {10.48550/arXiv.2208.02705},
}

@inproceedings{Kulkarni:2023:3PN,
  author =        {Kulkarni, Shreyas and Yin, Peng and
                   Scherer, Sebastian},
  booktitle =     {Proc. IROS},
  number =        {},
  pages =         {7202-7209},
  title =         {{360FusionNeRF}: Panoramic Neural Radiance Fields
                   with Joint Guidance},
  volume =        {},
  year =          {2023},
  doi =           {10.1109/IROS55552.2023.10341346},
}

@inproceedings{Chen:2023:PGS,
  author =        {Chen, Zheng and Cao, Yan-Pei and Guo, Yuan-Chen and
                   Wang, Chen and Shan, Ying and Zhang, Song-Hai},
  booktitle =     {Proc. NeurIPS},
  title =         {{PanoGRF}: generalizable spherical radiance fields
                   for wide-baseline panoramas},
  year =          {2023},
  doi =           {10.48550/arXiv.2306.01531},
}

@article{Wallingford:2024:ISL,
  author =        {Wallingford, Matthew and Bhattad, Anand and
                   Kusupati, Aditya and Ramanujan, Vivek and
                   Deitke, Matt and Kembhavi, Aniruddha and
                   Mottaghi, Roozbeh and Ma, Wei-Chiu and Farhadi, Ali},
  journal =       {Proc. NeurIPS},
  pages =         {17743--17760},
  title =         {From an image to a scene: Learning to imagine the
                   world from a million 360 videos},
  volume =        {37},
  year =          {2024},
  doi =           {10.48550/arXiv.2412.07770},
}

@article{Xia:2025:PLD,
  author =        {Xia, Yifei and Weng, Shuchen and Yang, Siqi and
                   Liu, Jingqi and Zhu, Chengxuan and Teng, Minggui and
                   Jia, Zijian and Jiang, Han and Shi, Boxin},
  journal =       {arXiv preprint arXiv:2505.22016},
  title =         {{PanoWan}: Lifting Diffusion Video Generation Models
                   to 360° with Latitude/Longitude-aware
                   Mechanisms},
  year =          {2025},
  doi =           {10.48550/arXiv.2505.22016},
}

@inproceedings{Yang:2025:LayerPano3D,
  author =        {Yang, Shuai and Tan, Jing and Zhang, Mengchen and
                   Wu, Tong and Wetzstein, Gordon and Liu, Ziwei and
                   Lin, Dahua},
  booktitle =     {Proc. SIGGRAPH Conference Papers},
  title =         {{LayerPano3D}: Layered {3D} Panorama for
                   Hyper-Immersive Scene Generation},
  year =          {2025},
  abstract =      {3D immersive scene generation is a challenging yet
                   critical task in computer vision and graphics. A
                   desired virtual 3D scene should 1) exhibit
                   omnidirectional view consistency, and 2) allow for
                   large-range exploration in complex scene hierarchies.
                   Existing methods either rely on successive scene
                   expansion via inpainting or employ panorama
                   representation to represent large FOV scene
                   environments. However, the generated scene suffers
                   from semantic drift during expansion and is unable to
                   handle occlusion among scene hierarchies. To tackle
                   these challenges, we introduce&nbsp;LayerPano3D, a
                   novel framework for full-view, explorable panoramic
                   3D scene generation from a single text prompt. Our
                   key insight is to decompose a reference 2D panorama
                   into multiple layers at different depth levels, where
                   each layer reveals the unseen space from the
                   reference views via diffusion
                   prior.&nbsp;LayerPano3D&nbsp;comprises multiple
                   dedicated designs: 1) We introduce a new panorama
                   dataset Upright360&nbsp;, comprising 9k high-quality
                   and upright panorama images, and finetune the
                   advanced Flux model on Upright360&nbsp;for
                   high-quality, upright and consistent panorama
                   generation related tasks. 2) We pioneer the Layered
                   3D Panorama as underlying representation to manage
                   complex scene hierarchies and lift it into 3D
                   Gaussians to splat detailed 360-degree
                   omnidirectional scenes with unconstrained viewing
                   paths. Extensive experiments demonstrate that our
                   framework generates state-of-the-art 3D panoramic
                   scene in both full view consistency and immersive
                   exploratory experience. We believe
                   that&nbsp;LayerPano3D&nbsp;holds promise for
                   advancing 3D panoramic scene creation with numerous
                   applications. More examples please visit our webpage:
                   ys-imtech.github.io/projects/LayerPano3D/},
  doi =           {10.1145/3721238.3730643},
}

@inproceedings{Huang:2025:Scene4U,
  author =        {Huang, Zilong and He, Jun and Ye, Junyan and
                   Jiang, Lihan and Li, Weijia and Chen, Yiping and
                   Han, Ting},
  booktitle =     {Proc. CVPR},
  pages =         {26723-26733},
  title =         {{Scene4U}: Hierarchical Layered {3D} Scene
                   Reconstruction from Single Panoramic Image for Your
                   Immerse Exploration},
  year =          {2025},
  doi =           {10.1109/CVPR52734.2025.02489},
}

@inproceedings{Yu:2025:Wonderworld,
  author =        {Yu, Hong-Xing and Duan, Haoyi and Herrmann, Charles and
                   Freeman, William T. and Wu, Jiajun},
  booktitle =     {Proc. CVPR},
  pages =         {5916-5926},
  title =         {{WonderWorld}: Interactive {3D} Scene Generation from
                   a Single Image},
  year =          {2025},
  doi =           {10.1109/CVPR52734.2025.00555},
}

@article{wang:2025:PIST,
  address =       {},
  author =        {Wang, Weiyu and Qing, Chunmei and Tan, Junpeng and
                   Xu, Xiangmin},
  journal =       {ACM Trans. Multimedia Comput. Commun. Appl.},
  number =        {7},
  publisher =     {},
  title =         {Multi-view Panoramic Image Style Transfer with
                   Multi-scale Attention and Global Sharing},
  volume =        {21},
  year =          {2025},
  abstract =      {Style transfer for panoramic images is a challenging
                   task, due to the problems associated with its unique
                   structure, including edge discontinuities, pole
                   distortion, fuzzy details, and memory limitation. In
                   this article, we propose a novel Multi-view
                   Transformation network for Panorama Style Transfer
                   (MuTPST). First, this architecture has a multi-view
                   panoramic transformation mechanism, which includes a
                   multi-view cubic projection and a multi-view
                   equirectangular re-projection of panoramic images.
                   This can address pole distortion and edge
                   discontinuity by skillfully applying multiple types
                   of projections and transformations. To capture
                   different levels of context and structure in the
                   stylization stage, we carefully design a multi-scale
                   attention content encoder, which can coordinate the
                   distribution of visual attention across space and
                   channels. Besides, by the sharing of global style
                   features in thumbnails and patches, MuTPST can
                   process ultra-high-resolution panoramic images (e.g.,
                   10,000 (times) 5,000 pixels) with limited GPU memory.
                   Extensive experiments illustrate that the proposed
                   method outperforms the state-of-the-art with a
                   discernible improvement in panoramic image style
                   transfer. More results and interactive features can
                   be found on .},
  doi =           {10.1145/3735137},
}

@article{Ye:2025:PAST,
  author =        {Wujian Ye and Yue Wang and Yijun Liu and Wenjie Lin and
                   Xin Xiang},
  journal =       {Journal of Visual Communication and Image
                   Representation},
  pages =         {104344},
  title =         {Panoramic Arbitrary Style Transfer with Deformable
                   Distortion Constraints},
  volume =        {106},
  year =          {2025},
  abstract =      {Neural style transfer is a prominent AI technique for
                   creating captivating visual effects and enhancing
                   user experiences. However, most current methods
                   inadequately handle panoramic images, leading to a
                   loss of original visual semantics and emotions due to
                   insufficient structural feature consideration. To
                   address this, a novel panorama arbitrary style
                   transfer method named PAST-Renderer is proposed by
                   integrating deformable convolutions and distortion
                   constraints. The proposed method can dynamically
                   adjust the position of the convolutional kernels
                   according to the geometric structure of the input
                   image, thereby better adapting to the spatial
                   distortions and deformations in panoramic images.
                   Deformable convolutions enable adaptive
                   transformations on a two-dimensional plane, enhancing
                   content and style feature extraction and fusion in
                   panoramic images. Distortion constraints adjust
                   content and style losses, ensuring semantic
                   consistency in salience, edge, and depth of field
                   with the original image. Experimental results show
                   significant improvements, with the PSNR (Peak
                   Signal-to-Noise Ratio) and SSIM (Structural
                   Similarity Index Measure) of stylized panoramic
                   images’ semantic maps increasing by approximately
                   2–4 dB and 0.1–0.3, respectively. Our method
                   PAST-Renderer performs better in both artistic and
                   realistic style transfer, preserving semantic
                   integrity with natural colors, realistic edge
                   details, and rich thematic content.},
  doi =           {https://doi.org/10.1016/j.jvcir.2024.104344},
}

@article{Shah:2025:VISPI,
  author =        {Shah, Uzair and Jashari, Sara and Tukur, Muhammad and
                   Househ, Mowafa and Schneider, Jens and
                   Pintore, Giovanni and Gobbetti, Enrico and
                   Agus, Marco},
  journal =       {IEEE CG\&A},
  number =        {},
  pages =         {1-14},
  title =         {Virtual Staging of Indoor Panoramic Images via
                   Multi-task Learning and Inverse Rendering},
  volume =        {},
  year =          {2025},
  doi =           {10.1109/MCG.2025.3605806},
}

@article{Zhi:2022:SSAD,
  address =       {},
  author =        {Zhi, Tiancheng and Chen, Bowei and Boyadzhiev, Ivaylo and
                   Kang, Sing Bing and Hebert, Martial and
                   Narasimhan, Srinivasa G.},
  journal =       {ACM TOG},
  number =        {4},
  publisher =     {},
  title =         {Semantically supervised appearance decomposition for
                   virtual staging from a single panorama},
  volume =        {41},
  year =          {2022},
  abstract =      {We describe a novel approach to decompose a single
                   panorama of an empty indoor environment into four
                   appearance components: specular, direct sunlight,
                   diffuse and diffuse ambient without direct sunlight.
                   Our system is weakly supervised by automatically
                   generated semantic maps (with floor, wall, ceiling,
                   lamp, window and door labels) that have shown success
                   on perspective views and are trained for panoramas
                   using transfer learning without any further
                   annotations. A GAN-based approach supervised by
                   coarse information obtained from the semantic map
                   extracts specular reflection and direct sunlight
                   regions on the floor and walls. These lighting
                   effects are removed via a similar GAN-based approach
                   and a semantic-aware inpainting step. The appearance
                   decomposition enables multiple applications including
                   sun direction estimation, virtual furniture
                   insertion, floor material replacement, and sun
                   direction change, providing an effective tool for
                   virtual home staging. We demonstrate the
                   effectiveness of our approach on a large and recently
                   released dataset of panoramas of empty homes.},
  doi =           {10.1145/3528223.3530148},
}

@inproceedings{Sener:2018:MLM,
  author =        {Sener, Ozan and Koltun, Vladlen},
  booktitle =     {Proc. NeurIPS},
  pages =         {525–536},
  title =         {Multi-task learning as multi-objective optimization},
  year =          {2018},
  doi =           {10.48550/arXiv.1810.04650},
}

@inproceedings{Kendall:2018:MLU,
  author =        {Kendall, Alex and Gal, Yarin and Cipolla, Roberto},
  booktitle =     {Proc. CVPR},
  pages =         {7482--7491},
  title =         {Multi-task learning using uncertainty to weigh losses
                   for scene geometry and semantics},
  year =          {2018},
  doi =           {10.1109/CVPR.2018.00781},
}

@inproceedings{Lee:2021:ICCV,
  author =        {Lee, Jae-Han and Lee, Chul and Kim, Chang-Su},
  booktitle =     {Proc. ICCV},
  pages =         {5107--5116},
  title =         {Learning multiple pixelwise tasks based on loss scale
                   balancing},
  year =          {2021},
}

@inproceedings{Dai:2023:IGB,
  author =        {Dai, Yanqi and Fei, Nanyi and Lu, Zhiwu},
  booktitle =     {Uncertainty in Artificial Intelligence},
  organization =  {PMLR},
  pages =         {496--506},
  title =         {Improvable gap balancing for multi-task learning},
  year =          {2023},
  doi =           {10.48550/arXiv.2307.15429},
}

@article{Liu:2021:CDG,
  author =        {Liu, Bo and Liu, Xingchao and Jin, Xiaojie and
                   Stone, Peter and Liu, Qiang},
  journal =       {Proc. NeurIPS},
  pages =         {18878--18890},
  title =         {Conflict-averse gradient descent for multi-task
                   learning},
  volume =        {34},
  year =          {2021},
  doi =           {10.48550/arXiv.2110.14048},
}

@inproceedings{Chen:2018:GGN,
  author =        {Chen, Zhao and Badrinarayanan, Vijay and Lee, Chen-Yu and
                   Rabinovich, Andrew},
  booktitle =     {Proc. ICML},
  organization =  {PMLR},
  pages =         {794--803},
  title =         {{GradNorm}: Gradient normalization for adaptive loss
                   balancing in deep multitask networks},
  year =          {2018},
  doi =           {10.48550/arXiv.1711.02257},
}

@article{Yu:2020:GSM,
  author =        {Yu, Tianhe and Kumar, Saurabh and Gupta, Abhishek and
                   Levine, Sergey and Hausman, Karol and Finn, Chelsea},
  journal =       {Proc. NeurIPS},
  pages =         {5824--5836},
  title =         {Gradient surgery for multi-task learning},
  volume =        {33},
  year =          {2020},
  doi =           {10.48550/arXiv.2001.06782},
}

@article{Bagdasarian:2025:3S3,
  author =        {Bagdasarian, Milena T and Knoll, Paul and Li, Y and
                   Barthel, Florian and Hilsmann, Anna and Eisert, Peter and
                   Morgenstern, Wieland},
  journal =       {Computer Graphics Forum},
  number =        {2},
  pages =         {e70078},
  title =         {{3DGS.zip}: A survey on 3D gaussian splatting
                   compression methods},
  volume =        {44},
  year =          {2025},
  doi =           {10.1111/cgf.70078},
}

@inproceedings{Slavcheva:2024:ADIP,
  author =        {Slavcheva, Mira and Gausebeck, Dave and Chen, Kevin and
                   Buchhofer, David and Sabik, Azwad and Ma, Chen and
                   Dhillon, Sachal and Brandt, Olaf and Dolhasz, Alan},
  booktitle =     {Proc. CVPRW},
  number =        {},
  pages =         {7384-7394},
  title =         {An Empty Room is All We Want: Automatic Defurnishing
                   of Indoor Panoramas},
  volume =        {},
  year =          {2024},
  doi =           {10.1109/CVPRW63382.2024.00734},
}

@inproceedings{Dolhasz:2025:defurnishing,
  author =        {Dolhasz, Alan and Ma, Chen and Gausebeck, Dave and
                   Chen, Kevin and Miller, Gregor and Hayne, Lucas and
                   Hovden, Gunnar and Sabik, Azwad and Brandt, Olaf and
                   Slavcheva, Mira},
  booktitle =     {Proc. CVPR},
  pages =         {6280--6290},
  title =         {Defurnishing with {X-Ray} Vision: Joint Removal of
                   Furniture from Panoramas and Mesh},
  year =          {2025},
  doi =           {10.1109/CVPRW67362.2025.00624},
}

@inproceedings{Liang:2025:POID,
  author =        {Liang, Ruofan and Gojcic, Zan and
                   Nimier-David, Merlin and Acuna, David and
                   Vijaykumar, Nandita and Fidler, Sanja and Wang, Zian},
  booktitle =     {Proc. ECCV},
  pages =         {446--465},
  title =         {Photorealistic Object Insertion with Diffusion-Guided
                   Inverse Rendering},
  year =          {2024},
  doi =           {10.1007/978-3-031-73030-6_25},
}

@inproceedings{Liu:2024:SGCIDM,
  author =        {Liu, Qingyang and You, Junqi and Wang, Jianting and
                   Tao, Xinhao and Zhang, Bo and Niu, Li},
  booktitle =     {Proc. CVPR},
  number =        {},
  pages =         {8121-8130},
  title =         {Shadow Generation for Composite Image Using Diffusion
                   Model},
  volume =        {},
  year =          {2024},
  doi =           {10.1109/CVPR52733.2024.00776},
}

@inproceedings{Xing:2025:luminet,
  author =        {Xing, Xiaoyan and Groh, Konrad and Karaoglu, Sezer and
                   Gevers, Theo and Bhattad, Anand},
  booktitle =     {Proc. CVPR},
  pages =         {442--452},
  title =         {Luminet: Latent intrinsics meets diffusion models for
                   indoor scene relighting},
  year =          {2025},
  doi =           {10.1109/CVPR52734.2025.00050},
}

@article{Zhou:2024:HH3,
  author =        {Zhou, Haiyang and Cheng, Xinhua and Yu, Wangbo and
                   Tian, Yonghong and Yuan, Li},
  journal =       {arXiv preprint arXiv:2407.15187},
  title =         {{HoloDreamer}: Holistic {3D} panoramic world
                   generation from text descriptions},
  year =          {2024},
  doi =           {10.48550/arXiv.2407.15187},
}

@article{Shen:2025:IllumiDiff,
  author =        {Shen, Shiyuan and Bao, Zhongyun and Xu, Wenju and
                   Xiao, Chunxia},
  journal =       {IEEE TVCG},
  number =        {10},
  pages =         {7752-7768},
  title =         {{IllumiDiff}: Indoor Illumination Estimation From a
                   Single Image With Diffusion Model},
  volume =        {31},
  year =          {2025},
  doi =           {10.1109/TVCG.2025.3553853},
}

@article{Lyu:2025:IntrinsicEdit,
  address =       {},
  author =        {Lyu, Linjie and Deschaintre, Valentin and
                   Hold-Geoffroy, Yannick and Hasan, Milos and
                   Yoon, Jae Shin and Leimkühler, Thomas and
                   Theobalt, Christian and Georgiev, Iliyan},
  journal =       {ACM Trans. Graph.},
  number =        {4},
  publisher =     {},
  title =         {{IntrinsicEdit}: Precise generative image
                   manipulation in intrinsic space},
  volume =        {44},
  year =          {2025},
  abstract =      {Generative diffusion models have advanced image
                   editing by delivering high-quality results through
                   intuitive interfaces such as prompts, scribbles, and
                   semantic drawing. However, these interfaces lack
                   precise control, and associated editing methods often
                   specialize in a single task. We introduce a versatile
                   workflow for a range of editing tasks which operates
                   in an intrinsic-image latent space, enabling
                   semantic, local manipulation with pixel precision
                   while automatically handling effects like reflections
                   and shadows. We build on the RGB↔X diffusion
                   framework and address its key deficiencies: the lack
                   of identity preservation and the need to update
                   multiple channels to achieve plausible results. We
                   propose an edit-friendly diffusion inversion and
                   prompt-embedding optimization to enable precise and
                   efficient editing of only the relevant channels. Our
                   method achieves identity preservation and resolves
                   global illumination, without requiring task-specific
                   model fine-tuning. We demonstrate state-of-the-art
                   performance across a variety of tasks on complex
                   images, including material adjustments, object
                   insertion and removal, global relighting, and their
                   combinations.},
  doi =           {10.1145/3731173},
}

@article{shinde:2023:systematic,
  author =        {Shinde, Yugandhar and Lee, Kyeongsuk and Kiper, Beyza and
                   Simpson, Makayla and Hasanzadeh, Sogand},
  journal =       {J. ITcon},
  number =        {21},
  pages =         {405--437},
  title =         {A Systematic Literature Review on 360°
                   Panoramic Applications in Architecture, Engineering,
                   and Construction ({AEC}) Industry},
  volume =        {28},
  year =          {2023},
}

@InProceedings{li:2025:creating,
  author={Li, Liang and Li, Yue},
  title={Creating Panorama Virtual Tour Systems for the Built Environment: A Practitioner Perspective},
  booktitle={Advances in the Integration of Technology and the Built Environment},
  year=2025,
  pages={101--109},
}

@inproceedings{Armeni:2016:3SP,
  author =        {Armeni, Iro and Sener, Ozan and Zamir, Amir R. and
                   Jiang, Helen and Brilakis, Ioannis and
                   Fischer, Martin and Savarese, Silvio},
  booktitle =     {Proc. CVPR},
  pages =         {1534--1543},
  title =         {{3D} Semantic Parsing of Large-scale Indoor Spaces},
  year =          {2016},
  doi =           {10.1109/CVPR.2016.170},
}

@inproceedings{Armeni:2019:3SG,
  author =        {Armeni, Iro and He, Zhi-Yang and Gwak, JunYoung and
                   Zamir, Amir R and Fischer, Martin and Malik, Jitendra and
                   Savarese, Silvio},
  booktitle =     {Proc. CVPR},
  pages =         {5664--5673},
  title =         {{3D Scene Graph}: A structure for unified semantics,
                   {3D} space, and camera},
  year =          {2019},
  doi =           {10.1109/ICCV.2019.00576},
}

@inproceedings{Xia:2018:GER,
  author =        {Xia, Fei and R. Zamir, Amir and He, Zhiyang and
                   Sax, Alexander and Malik, Jitendra and
                   Savarese, Silvio},
  booktitle =     {Proc. CVPR},
  title =         {{Gibson Env}: real-world perception for embodied
                   agents},
  year =          {2018},
  doi =           {10.1109/CVPR.2018.00945},
}

@inproceedings{Moulon:2016:OOM,
  author =        {Moulon, Pierre and Monasse, Pascal and
                   Perrot, Romuald and Marlet, Renaud},
  booktitle =     {International Workshop on Reproducible Research in
                   Pattern Recognition},
  organization =  {Springer},
  pages =         {60--74},
  title =         {{OpenMVG}: Open multiple view geometry},
  year =          {2016},
  doi =           {10.1007/978-3-319-56414-2_5},
}

@article{Zhuang:2023:SPDET,
  author =        {Zhuang, Chuanqing and Lu, Zhengda and Wang, Yiqun and
                   Xiao, Jun and Wang, Ying},
  journal =       {IEEE TPAMI},
  number =        {10},
  pages =         {12474--12489},
  title =         {{SPDET}: Edge-aware self-supervised panoramic depth
                   estimation transformer with spherical geometry},
  volume =        {45},
  year =          {2023},
  doi =           {10.1109/TPAMI.2023.3272949},
}

@article{Li:2025:SIS,
  author =        {Li, Tong and Zhang, Zhaoxuan and Wang, Yuxin and
                   Cui, Yan and Li, Yuqi and Zhou, Dongsheng and
                   Yin, Baocai and Yang, Xin},
  journal =       {The Visual Computer},
  number =        {3},
  pages =         {1891--1905},
  title =         {Self-supervised indoor scene point cloud completion
                   from a single panorama},
  volume =        {41},
  year =          {2025},
  doi =           {10.1007/s00371-024-03509-w},
}

@inproceedings{Deb:2024:ZTT,
  author =        {Deb, Tonmoay and Wang, Lichen and Bessinger, Zachary and
                   Khosravan, Naji and Penner, Eric and Kang, Sing Bing},
  booktitle =     {Proc. CVPR Workshops},
  pages =         {2050--2059},
  title =         {{ZInD-Tell}: Towards Translating Indoor Panoramas
                   into Descriptions},
  year =          {2024},
  doi =           {10.1109/CVPRW63382.2024.00210},
}

@article{Khan:2025:PSD,
  author =        {Khan, Mariia and Qiu, Yue and Cong, Yuren and
                   Abu-Khalaf, Jumana and Suter, David and
                   Rosenhahn, Bodo},
  journal =       {IEEE Access},
  number =        {},
  pages =         {72456-72476},
  title =         {{PanoSCU}: A Simulation-Based Dataset for Panoramic
                   Indoor Scene Understanding},
  volume =        {13},
  year =          {2025},
  doi =           {10.1109/ACCESS.2025.3561055},
}

@article{Kolve:2017:AI3,
  author =        {Kolve, Eric and Mottaghi, Roozbeh and Han, Winson and
                   VanderBilt, Eli and Weihs, Luca and Herrasti, Alvaro and
                   Deitke, Matt and Ehsani, Kiana and Gordon, Daniel and
                   Zhu, Yuke and Kembhavi, Aniruddha and Gupta, Abhinav and
                   Farhadi, Ali},
  journal =       {arXiv preprint arXiv:1712.05474},
  title =         {{AI2-THOR}: An interactive {3D} environment for
                   visual {AI}},
  year =          {2017},
  doi =           {10.48550/arXiv.1712.05474},
}

@article{sgrenzaroli:2022:indoor,
  author =        {Sgrenzaroli, M and Ortiz Barrientos, J and Vassena, G and
                   Sanchez, A and Ciribini, A and Mastrolembo Ventura, S and
                   Comai, S},
  journal =       {International Archives of the Photogrammetry, Remote
                   Sensing and Spatial Information Sciences},
  number =        {1-2022},
  pages =         {121--127},
  title =         {Indoor Mobile Mapping Systems and (BIM) digital
                   models for construction progress monitoring},
  volume =        {43},
  year =          {2022},
  doi =           {10.5194/isprs-archives-XLIII-B1-2022-121-2022},
}

@article{de:2022:dissemination,
  author =        {De Fino, Mariella and Bruno, Silvana and
                   Fatiguso, Fabio},
  journal =       {Virtual Archaeology Review},
  number =        {26},
  pages =         {88--102},
  title =         {Dissemination, assessment and management of historic
                   buildings by thematic virtual tours and {3D} models},
  volume =        {13},
  year =          {2022},
  doi =           {10.4995/var.2022.15426},
}

@article{de:2020:virtual,
  author =        {De Fino, Mariella and Ceppi, Claudia and
                   Fatiguso, Fabio},
  journal =       {The International Archives of the Photogrammetry,
                   Remote Sensing and Spatial Information Sciences},
  pages =         {473--480},
  publisher =     {Copernicus GmbH},
  title =         {Virtual tours and informational models for improving
                   territorial attractiveness and the smart management
                   of architectural heritage: The {3D-IMP-ACT} project},
  volume =        {44},
  year =          {2020},
  doi =           {10.5194/isprs-archives-XLIV-M-1-2020-473-2020},
}

@inproceedings{kim:2019:comparing,
  author =        {Kim, Jeffrey S and Leathem, Tom and Liu, Junshan},
  booktitle =     {55th ASC Annual International Conference Proceedings},
  pages =         {221--228},
  title =         {Comparing virtual reality modalities and 360
                   photography in a construction management classroom},
  year =          {2019},
  url =           {http://ascpro0.ascweb.org/archives/cd/2019/paper/
                  CERT284002019.pdf},
}

@article{eiris:2022:ivisit,
  author =        {Eiris, Ricardo and Wen, Jing and Gheisari, Masoud},
  journal =       {Computers \& Education},
  pages =         {104365},
  publisher =     {Elsevier},
  title =         {iVisit-Collaborate: Collaborative problem-solving in
                   multiuser 360-degree panoramic site visits},
  volume =        {177},
  year =          {2022},
  doi =           {10.1016/j.compedu.2021.104365},
}

@article{eiris:2020:desktop,
  author =        {Eiris, Ricardo and Gheisari, Masoud and
                   Esmaeili, Behzad},
  journal =       {Automation in construction},
  pages =         {102969},
  publisher =     {Elsevier},
  title =         {Desktop-based safety training using 360-degree
                   panorama and static virtual reality techniques: A
                   comparative experimental study},
  volume =        {109},
  year =          {2020},
  doi =           {10.1016/j.autcon.2019.102969},
}

@inproceedings{subramanian:2019:using,
  author =        {Subramanian, Praseedha and Gheisari, Masoud},
  booktitle =     {Proc. Associated Schools of Construction Annual
                   Conference},
  pages =         {743--750},
  title =         {Using 360-degree panoramic photogrammetry and laser
                   scanning techniques to create point cloud data: A
                   comparative pilot study},
  year =          {2019},
  url =           {http://ascpro0.ascweb.org/archives/cd/2019/paper/
                  CPRT305002019.pdf},
}

@article{afsari:2021:fundamentals,
  author =        {Afsari, Kereshmeh and Halder, Srijeet and
                   Ensafi, Mahnaz and DeVito, Stephen and
                   Serdakowski, John},
  journal =       {EPiC series in built environment},
  pages =         {274--283},
  title =         {Fundamentals and prospects of four-legged robot
                   application in construction progress monitoring},
  volume =        {2},
  year =          {2021},
  doi =           {10.29007/cdpd},
}

@inproceedings{halder:2021:methodology,
  author =        {Halder, Srijeet and Afsari, Kereshmeh and
                   Serdakowski, John and DeVito, Stephen},
  booktitle =     {Proc. ISARC},
  organization =  {IAARC Publications},
  pages =         {17--24},
  title =         {A methodology for BIM-enabled automated reality
                   capture in construction inspection with quadruped
                   robots},
  volume =        {38},
  year =          {2021},
  doi =           {10.22260/ISARC2021/0005},
}

@article{isingizwe:2024:enhancing,
  author =        {Isingizwe, Josiane and Eiris, Ricardo and
                   Al-Bayati, Ahmed Jalil},
  journal =       {Safety Science},
  pages =         {106631},
  publisher =     {Elsevier},
  title =         {Enhancing safety training engagement through
                   immersive storytelling: A case study in the
                   residential construction},
  volume =        {179},
  year =          {2024},
  doi =           {10.1016/j.ssci.2024.106631},
}

@article{Wang:2025:building,
  author =        {Shuo Wang and Yujie Lu and Zongjun Xia and
                   Zhifei Chen and Yifei Wang and Lijian Zhong and
                   Tao Zhong},
  journal =       {Advanced Engineering Informatics},
  pages =         {103629},
  title =         {Integrating visual-{SLAM} and multi-view panoramas
                   for efficient indoor {3D} layout reconstruction in
                   building projects},
  volume =        {68},
  year =          {2025},
  abstract =      {With the increasing demand for digital delivery in
                   the Architecture, Engineering, and Construction (AEC)
                   industry, efficient worksite inspection and
                   supervision have become pivotal for effective project
                   management. This study introduces an efficient
                   approach for 3D indoor reconstruction in building
                   projects, enabling automated layout estimation and
                   visualization through panoramic site inspections. The
                   primary contribution lies in integrating visual-SLAM
                   and multi-view panorama techniques for efficient
                   indoor 3D layout reconstruction, encompassing three
                   core components: an optimal panoramic capture point
                   identification technique, a precise spatial dimension
                   recovery method for single-room layouts, and a
                   timestamp-based model splicing strategy. The
                   OpenVSLAM-based algorithm is employed to identify
                   optimal panoramic capture points during dynamic site
                   inspections, ensuring efficient and comprehensive
                   data collection. An integrated panoramic data
                   processing method incorporating occlusion removal and
                   data augmentation enables the HorizonNet algorithm to
                   achieve precise 3D layout reconstruction, with room
                   boundary estimation attaining 83.03% 3D IoU, 0.74%
                   corner error, and 2.72% pixel error. The proposed
                   model splicing method effectively integrates multiple
                   room segments into unified 3D models, facilitating
                   comprehensive spatial coverage. The resulting 3D
                   models presented in point cloud format are evaluated
                   based on four key metrics: space area error, single
                   wall offset error, single wall length error, and
                   elevation error. By combining scaled indoor layout
                   reconstruction with efficiency capabilities, the
                   proposed method enhances both the mobility and
                   practicality of panoramic video-driven 3D
                   reconstruction in building engineering, thus
                   supporting more efficient construction site
                   management and digital project delivery.},
  doi =           {10.1016/j.aei.2025.103629},
  issn =          {1474-0346},
}

@article{dieb2024interactive,
  author =        {Dieb, Rida and Alsalloum, Ataa and Webb, Nicholas},
  journal =       {Built Heritage},
  number =        {1},
  pages =         {18},
  publisher =     {Springer},
  title =         {Interactive 360° media for the dissemination
                   of endangered world heritage sites: the ancient city
                   of Palmyra in Syria},
  volume =        {8},
  year =          {2024},
  doi =           {10.1186/s43238-024-00126-3},
}

@article{Fang:2023:FEM,
  author =        {Fang, Xin and Li, Heng and Wu, Haitao and Fan, Lang and
                   Kong, Ting and Wu, Yue},
  journal =       {Engineering Applications of Artificial Intelligence},
  pages =         {106733},
  publisher =     {Elsevier},
  title =         {A fast end-to-end method for automatic interior
                   progress evaluation using panoramic images},
  volume =        {126},
  year =          {2023},
  doi =           {10.1016/j.engappai.2023.106733},
}

@article{Awais:2025:FMD,
  author =        {Awais, Muhammad and Naseer, Muzammal and Khan, Salman and
                   Anwer, Rao Muhammad and Cholakkal, Hisham and
                   Shah, Mubarak and Yang, Ming-Hsuan and
                   Khan, Fahad Shahbaz},
  journal =       {IEEE Transactions on Pattern Analysis and Machine
                   Intelligence},
  number =        {4},
  pages =         {2245-2264},
  title =         {Foundation Models Defining a New Era in Vision: A
                   Survey and Outlook},
  volume =        {47},
  year =          {2025},
  doi =           {10.1109/TPAMI.2024.3506283},
}

@inproceedings{Chen:2020:GPP,
  author =        {Chen, Mark and Radford, Alec and Child, Rewon and
                   Wu, Jeffrey and Jun, Heewoo and Luan, David and
                   Sutskever, Ilya},
  booktitle =     {Proc. PMLR},
  pages =         {1691--1703},
  title =         {Generative pretraining from pixels},
  year =          {2020},
  url =           {https://proceedings.mlr.press/v119/chen20s.html},
}

@article{Naveed:2025:COL,
  author =        {Naveed, Humza and Khan, Asad Ullah and Qiu, Shi and
                   Saqib, Muhammad and Anwar, Saeed and Usman, Muhammad and
                   Akhtar, Naveed and Barnes, Nick and Mian, Ajmal},
  journal =       {ACM Trans. Intell. Syst. Technol.},
  number =        {5},
  title =         {A Comprehensive Overview of Large Language Models},
  volume =        {16},
  year =          {2025},
  doi =           {10.1145/3744746},
}

@inproceedings{Cao:2025:SSC,
  author =        {Cao, Zidong and Zhu, Jinjing and Ai, Hao and
                   Jiang, Lutao and Lyu, Yuanhuiyi and Xiong, Hui},
  booktitle =     {Proc. NeurIPS},
  title =         {{ST$^2$360D}: Spatial-to-Temporal Consistency for
                   Training-free 360 Monocular Depth Estimation},
  year =          {2025},
  url =           {https://neurips.cc/virtual/2025/loc/san-diego/poster/
                  117039},
}

@article{Luo:2020:CVD,
  author =        {Luo, Xuan and Huang, Jia-Bin and Szeliski, Richard and
                   Matzen, Kevin and Kopf, Johannes},
  journal =       {ACM Trans. Graph.},
  number =        {4},
  title =         {Consistent video depth estimation},
  volume =        {39},
  year =          {2020},
  doi =           {10.1145/3386569.3392377},
}

@inproceedings{Chen:2025:VDA,
  author =        {Chen, Sili and Guo, Hengkai and Zhu, Shengnan and
                   Zhang, Feihu and Huang, Zilong and Feng, Jiashi and
                   Kang, Bingyi},
  booktitle =     {Proc. CVPR},
  pages =         {22831-22840},
  title =         {{Video Depth Anything}: Consistent Depth Estimation
                   for Super-Long Videos},
  year =          {2025},
  doi =           {10.1109/CVPR52734.2025.02126},
}

@inproceedings{Peng:2023:HDE,
  author =        {Peng, Chi-Han and Zhang, Jiayao},
  booktitle =     {Proc. WACV},
  pages =         {3115--3124},
  title =         {High-Resolution Depth Estimation for 360°
                   Panoramas through Perspective and Panoramic Depth
                   Images Registration},
  year =          {2023},
  doi =           {10.1109/WACV56688.2023.00313},
}

@inproceedings{Kirillov:2023:SA,
  author =        {Kirillov, Alexander and Mintun, Eric and
                   Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and
                   Gustafson, Laura and Xiao, Tete and
                   Whitehead, Spencer and Berg, Alexander C. and
                   Lo, Wan-Yen and Dollár, Piotr and Girshick, Ross},
  booktitle =     {Proc. ICCV},
  number =        {},
  pages =         {3992-4003},
  title =         {Segment Anything},
  volume =        {},
  year =          {2023},
  doi =           {10.1109/ICCV51070.2023.00371},
}

@inproceedings{Xu:2021:ESO,
  author =        {Xu, Mengde and Zhang, Zheng and Hu, Han and
                   Wang, Jianfeng and Wang, Lijuan and Wei, Fangyun and
                   Bai, Xiang and Liu, Zicheng},
  booktitle =     {Proc. ICCV},
  number =        {},
  pages =         {3040-3049},
  title =         {End-to-End Semi-Supervised Object Detection with Soft
                   Teacher},
  volume =        {},
  year =          {2021},
  doi =           {10.1109/ICCV48922.2021.00305},
}

@inproceedings{Yang:2024:DAU,
  author =        {Yang, Lihe and Kang, Bingyi and Huang, Zilong and
                   Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang},
  booktitle =     {Proc. CVPR},
  number =        {},
  pages =         {10371-10381},
  title =         {{Depth Anything}: Unleashing the Power of Large-Scale
                   Unlabeled Data},
  volume =        {},
  year =          {2024},
  doi =           {10.1109/CVPR52733.2024.00987},
}

@article{Yang:2024:DAV,
  author =        {Yang, Lihe and Kang, Bingyi and Huang, Zilong and
                   Zhao, Zhen and Xu, Xiaogang and Feng, Jiashi and
                   Zhao, Hengshuang},
  journal =       {Proc. NeurIPS},
  pages =         {21875--21911},
  title =         {{Depth Anything V2}},
  volume =        {37},
  year =          {2024},
  doi =           {10.52202/079017-0688},
}

@article{Liu:2025:TUG,
  author =        {Liu, Xu and Zhou, Tong and Wang, Chong and
                   Wang, Yuping and Wang, Yuanxin and Cao, Qinjingwen and
                   Du, Weizhi and Yang, Yonghuan and He, Junjun and
                   Qiao, Yu and others},
  journal =       {The Visual Computer},
  number =        {5},
  pages =         {3371--3412},
  title =         {Toward the unification of generative and
                   discriminative visual foundation model: a survey},
  volume =        {41},
  year =          {2025},
  doi =           {10.1007/s00371-024-03608-8},
}

@article{Kaplan:2020:SLN,
  author =        {Kaplan, Jared and McCandlish, Sam and Henighan, Tom and
                   Brown, Tom B and Chess, Benjamin and Child, Rewon and
                   Gray, Scott and Radford, Alec and Wu, Jeffrey and
                   Amodei, Dario},
  journal =       {arXiv preprint arXiv:2001.08361},
  title =         {Scaling laws for neural language models},
  year =          {2020},
  doi =           {10.48550/arXiv.2001.08361},
}

@inproceedings{Brown:2020:LMF,
  author =        {Brown, Tom B. and Mann, Benjamin and Ryder, Nick and
                   Subbiah, Melanie and Kaplan, Jared and
                   Dhariwal, Prafulla and Neelakantan, Arvind and
                   Shyam, Pranav and Sastry, Girish and Askell, Amanda and
                   Agarwal, Sandhini and Herbert-Voss, Ariel and
                   Krueger, Gretchen and Henighan, Tom and Child, Rewon and
                   Ramesh, Aditya and Ziegler, Daniel M. and Wu, Jeffrey and
                   Winter, Clemens and Hesse, Christopher and Chen, Mark and
                   Sigler, Eric and Litwin, Mateusz and Gray, Scott and
                   Chess, Benjamin and Clark, Jack and
                   Berner, Christopher and McCandlish, Sam and
                   Radford, Alec and Sutskever, Ilya and Amodei, Dario},
  booktitle =     {Proc. NeurIPS},
  pages =         {1877--1901},
  title =         {Language models are few-shot learners},
  volume =        {33},
  year =          {2020},
  doi =           {10.48550/arXiv.2005.14165},
}

@article{Maslej:2025:AII,
  author =        {Maslej, Nestor and Fattorini, Loredana and
                   Perrault, Raymond and Gil, Yolanda and Parli, Vanessa and
                   Kariuki, Njenga and Capstick, Emily and Reuel, Anka and
                   Brynjolfsson, Erik and Etchemendy, John and others},
  journal =       {arXiv preprint arXiv:2504.07139},
  title =         {Artificial intelligence index report 2025},
  year =          {2025},
  doi =           {10.48550/arXiv.2310.03715},
}

@article{Cottier:2025:RCT,
  author =        {Cottier, Ben and Rahman, Robi and Fattorini, Loredana and
                   Maslej, Nestor and Besiroglu, Tamay and Owen, David},
  journal =       {arXiv preprint arXiv:2405.21015},
  title =         {The rising costs of training frontier AI models},
  year =          {2025},
  doi =           {10.48550/arXiv.2405.21015},
}

@article{Bommasani:2024:CGO,
  author =        {Bommasani, Rishi and Kapoor, Sayash and Klyman, Kevin and
                   Longpre, Shayne and Ramaswami, Ashwin and
                   Zhang, Daniel and Schaake, Marietje and Ho, Daniel E and
                   Narayanan, Arvind and Liang, Percy},
  journal =       {Science},
  number =        {6718},
  pages =         {151--153},
  title =         {Considerations for governing open foundation models},
  volume =        {386},
  year =          {2024},
  doi =           {10.1126/science.adp1848},
}


%====================================================================
% DATASETS
%====================================================================

@misc{Data:Matterport3D:2017,
  author =        {Matterport},
  note =          {[Online; accessed: 2025-09-16]},
  title =         {{Matterport3D}},
  year =          {2017},
  howpublished = {Public dataset},
  url =           {https://github.com/niessner/Matterport},
}

@misc{Data:MatterportLayout:2019,
  author =        {Zou, Chuhang and Su, Jheng-Wei and Peng, Chi-Han and
                   Colburn, Alex and Shan, Qi and Wonka, Peter and
                   Chu, Hung-Kuo and Hoiem, Derek},
  note =          {[Online; accessed: 2025-09-16]},
  title =         {{MatterportLayout}},
  howpublished = {Public dataset},
  year =          {2017},
  url =           {https://github.com/ericsujw/Matterport3DLayoutAnnotation},
}

@misc{Data:360MonoDepthM3D:2022,
  author =        {Rey-Area, M. and Yuan, M. and Richardt, C.},
  note =          {[Online; accessed 2025-09-17]},
  title =         {{360MonoDepth Matterport3D}},
  howpublished = {Public dataset},
  year =          {2022},
  url =           {https://researchdata.bath.ac.uk/1126/},
}

@misc{Data:360MonoDepthReplica:2022,
  author =        {Rey-Area, M. and Yuan, M. and Richardt, C.},
  note =          {[Online; accessed 2025-09-17]},
  title =         {{360MonoDepth Replica}},
  howpublished = {Public dataset},
  year =          {2022},
  url =           {https://manurare.github.io/360monodepth/},
}

@misc{Data:2D-3D-S:2017,
  author =        {Stanford-University},
  note =          {[Online; accessed: 2025-09-16]},
  title =         {{BuildingParser Dataset}},
  howpublished = {Public dataset},
  year =          {2017},
  url =           {https://sdss.redivis.com/datasets/9q3m-9w5pa1a2h},
}

@misc{Data:Structured3D:2020,
  author =        {Jia Zheng and Junfei Zhang and Jing Li and Rui Tang and
                   Shenghua Gao and Zihan Zhou},
  note =          {[Online; accessed: 2025-09-16]},
  title =         {{Indoor3Dmapping dataset}},
  howpublished = {Public dataset},
  year =          {2020},
  url =           {https://structured3d-dataset.org/},
}

@misc{Data:PanoContext:2014,
  author =        {Zhang, Yinda and Song, Shuran and Tan, Ping and
                   Xiao, Jianxiong},
  note =          {[Online; accessed: 2025-09-16]},
  title =         {{PanoContext dataset}},
  howpublished = {Public dataset},
  year =          {2014},
  url =           {https://panocontext.cs.princeton.edu/},
}

@misc{Data:ZInD:2021,
  author =        {Cruz, Steve and Hutchcroft, Will and Li, Yuguang and
                   Khosravan, Naji and Boyadzhiev, Ivaylo and
                   Kang, Sing Bing},
  note =          {[Online; accessed: 2025-09-16]},
  title =         {{Zillow Indoor Dataset (ZInD)}},
  howpublished = {Public dataset},
  year =          {2021},
  url =           {https://github.com/zillow/zind},
}

@misc{Data:ZInDTell:2024,
  author =        {Deb, Tonmoay and Wang, Lichen and Bessinger, Zachary and
                   Khosravan, Naji and Penner, Eric and Kang, Sing Bing},
  note =          {[Online; accessed: 2025-09-18]},
  title =         {{ZInD-Tell Dataset}},
  howpublished = {Public dataset},
  year =          {2024},
  url =           {https://github.com/zillow/zindtell},
}

@misc{Data:PNVS:2021,
  author =        {Xu, Jiale and Zheng, Jia and Xu, Yanyu and Tang, Rui and
                   Gao, Shenghua},
  note =          {[Online; accessed: 2025-09-16]},
  title =         {{PNVS Dataset}},
  howpublished = {Public dataset},
  year =          {2021},
  url =           {https://github.com/bluestyle97/PNVS},
}

@misc{Data:SKI360:2020,
  author =        {ShanghaiTech-University and Kujiale-com},
  note =          {[Online; accessed 2025-09-16]},
  title =         {{Shanghaitech-Kujiale Indoor 360° (SKI360)
                   dataset}},
  year =          {2020},
  howpublished = {Public dataset},
  url =           {https://svip-lab.github.io/dataset/indoor_360.html},
}

@misc{Data:Pano3D:2021,
  author =        {Albanis, Georgios and Zioulis, Nikolaos and
                   Drakoulis, Petros and Gkitsas, Vasileios and
                   Sterzentsenko, Vladimiros and Alvarez, Federico and
                   Zarpalas, Dimitrios and Daras, Petros},
  note =          {[Online; accessed 2025-09-17]},
  title =         {{Pano3D}},
  howpublished = {Public dataset},
  year =          {2021},
  url =           {https://vcl3d.github.io/Pano3D/download/},
}

@misc{Data:Gibson:2018,
  author =        {Xia, Fei and R. Zamir, Amir and He, Zhiyang and
                   Sax, Alexander and Malik, Jitendra and
                   Savarese, Silvio},
  note =          {[Online; accessed 2025-09-17]},
  title =         {{GibsonV2}},
  howpublished = {Public dataset},
  year =          {2018},
  url =           {https://github.com/StanfordVL/GibsonEnv/tree/master/gibson/data},
}

@misc{Data:Replica:2019,
  author =        {Julian Straub and Thomas Whelan and Lingni Ma and
                   Yufan Chen and Erik Wijmans and Simon Green and
                   Jakob J. Engel and Raul Mur-Artal and Carl Ren and
                   Shobhit Verma and Anton Clarkson and Mingfei Yan and
                   Brian Budge and Yajie Yan and Xiaqing Pan and
                   June Yon and Yuyang Zou and Kimberly Leon and
                   Nigel Carter and Jesus Briales and Tyler Gillingham and
                   Elias Mueggler and Luis Pesqueira and Manolis Savva and
                   Dhruv Batra and Hauke M. Strasdat and Renzo De Nardi and
                   Michael Goesele and Steven Lovegrove and
                   Richard Newcombe},
  note =          {[Online; accessed 2025-09-17]},
  title =         {{The Replica Dataset}},
  howpublished = {Public dataset},
  year =          {2019},
  url =           {https://github.com/facebookresearch/Replica-Dataset},
}

@misc{Data:3DSceneGraph:2019,
  author =        {Armeni, Iro and He, Zhi-Yang and Gwak, JunYoung and
                   Zamir, Amir R and Fischer, Martin and Malik, Jitendra and
                   Savarese, Silvio},
  note =          {[Online; accessed 2025-09-17]},
  title =         {{The 3DSceneGraph Dataset}},
  howpublished = {Public dataset},
  year =          {2019},
  url =           {https://github.com/StanfordVL/3DSceneGraph/},
}

@misc{Data:PanoSCU:2025,
  author =        {Mariia Khan and Jumana Abu-Khalaf and David Suter},
  note =          {[Online; accessed: 2025-04-16]},
  title =         {{PanoSCU Dataset}},
  howpublished = {Public dataset},
  year =          {2025},
  url =           {https://doi.org/10.25958/4p6j-z657},
}

@misc{Data:FutureHouse:2022,
  author =        {Li, Zhen and Wang, Lingli and Huang, Xiang and
                   Pan, Cihui and Yang, Jiaqi},
  note =          {[Online; accessed: 2025-10-09]},
  title =         {{FutureHouse Dataset}},
  howpublished = {Public dataset},
  year =          {2022},
  url =           {https://github.com/LZleejean/FutureHouse},
}

@misc{Data:I3D:2022,
  author =        {Sánchez Alcázar, Armando Arturo and
                   Giovanni Pintore and Matteo Sgrenzaroli},
  note =          {[Online; accessed: 2025-09-17]},
  title =         {{Indoor3Dmapping dataset}},
  howpublished = {Public dataset},
  year =          {2022},
  url =           {https://doi.org/10.5281/zenodo.6367381},
}

