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360BEV: Panoramic Semantic Mapping for Indoor Bird’s-Eye View



In this work, mapping from 360° panoramas to BEV semantics, the 360BEV task, is established for the first time to achieve holistic representations of indoor scenes in a top-down view. Instead of relying on narrow-FoV image sequences, a panoramic image with depth information is sufficient to generate a holistic BEV semantic map. To benchmark 360BEV, we present two indoor datasets, 360BEV-Matterport and 360BEV-Stanford, both of which include egocentric panoramic images and semantic segmentation labels, as well as allocentric semantic maps.

For more details, please check our paper.


  • 03/2023, init repository.
  • 04/2023, release datasets.
  • 04/2023, release code and models.

360BEV datasets

Prepare datasets:

Extended datasets:

  • 360BEV-Stanford (Coming soon...)
  • 360BEV-Matterport (Coming soon...)

Data statistics:

Dataset Scene Room Frame Category
train 5 215 1,040 13
val 1 55 373 13
360BEV-Stanford 6 270 1,413 13
train 61 -- 7,829 20
val 7 -- 772 20
test 18 -- 2,014 20
360BEV-Matterport 86 2,030 10,615 20

360Mapper model


Results and weights


Method Backbone Acc mRecall mPrecision mIoU weights
Ours MiT-B0 92.07 50.14 65.37 42.42 Coming soon...
Ours MiT-B2 92.80 53.56 67.72 45.78 Coming soon...
Ours MSCA-B 92.67 55.02 68.02 46.44 Coming soon...


Method Backbone Acc mRecall mPrecision mIoU weights
Ours MiT-B0 75.44 48.80 56.01 36.98 Coming soon...
Ours MiT-B2 78.80 59.54 59.97 44.32 Coming soon...
Ours MSCA-B 78.93 60.51 62.83 46.31 Coming soon...


Coming soon...


This repository is under the Apache-2.0 license. For commercial use, please contact with the authors.


If you are interested in this work, please cite the following works:

  title={360BEV: Panoramic Semantic Mapping for Indoor Bird's-Eye View}, 
  author={Teng, Zhifeng and Zhang, Jiaming and Yang, Kailun and Peng, Kunyu and Shi, Hao and Reiß, Simon and Cao, Ke and Stiefelhagen, Rainer},
  journal={arXiv preprint arXiv:2303.11910},
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