Data

SAOM

Edith Cowan University
Mariia Khan (Aggregated by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25958/b7p2-z351&rft.title=SAOM&rft.identifier=10.25958/b7p2-z351&rft.publisher=Edith Cowan University&rft.description=The SAOM dataset is created for the evaluation of the whole-object semantic segmentation in embodied AI indoor environments. The SAOM dataset is tailored for segmentation in dynamic embodied environments, focusing on interactable objects. It includes 54 object classes, all of which are either `pickupable’, `openable’, or `receptacles`. Unlike static-object datasets, the objects in SAOM can undergo transformations, such as being opened, closed, or moved.&rft.creator=Mariia Khan&rft.date=2026&rft.relation=https://doi.org/10.48550/arXiv.2403.10780&rft_rights= http://creativecommons.org/licenses/by-nc/4.0/&rft_subject=semantic segmentation&rft_subject=whole-object mask&rft_subject=Computer Sciences&rft.type=dataset&rft.language=English Access the data
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Full description

The SAOM dataset is created for the evaluation of the whole-object semantic segmentation in embodied AI indoor environments. The SAOM dataset is tailored for segmentation in dynamic embodied environments, focusing on interactable objects. It includes 54 object classes, all of which are either `pickupable’, `openable’, or `receptacles`. Unlike static-object datasets, the objects in SAOM can undergo transformations, such as being opened, closed, or moved.

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Subjects
Computer Sciences | semantic segmentation | whole-object mask |

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Identifiers
ACN 633 798 857