mlsanityData to 14 Oct 2026

Offset-Guided Attention Network for Room-Level Aware Floor Plan Segmentation

arXiv 2210.17411 ↗First posted 22 Oct 2022cs.CVcited by 0

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Abstract (arXiv)

Recognition of floor plans has been a challenging and popular task. Despite that many recent approaches have been proposed for this task, they typically fail to make the room-level unified prediction. Specifically, multiple semantic categories can be assigned in a single room, which seriously limits their visual quality and applicability. In this paper, we propose a novel approach to recognize the floor plan layouts with a newly proposed Offset-Guided Attention mechanism to improve the semantic consistency within a room. In addition, we present a Feature Fusion Attention module that leverages the channel-wise attention to encourage the consistency of the room, wall, and door predictions, further enhancing the room-level semantic consistency. Experimental results manifest our approach is able to improve the room-level semantic consistency and outperforms the existing works both qualitatively and quantitatively.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 28 of its 51 references that are in the map. First posted on arXiv 22 Oct 2022.

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28 of 51 references.

References · 28

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16 more
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