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

TL;DR

This paper introduces an Offset-Guided Attention network that enhances room-level semantic consistency in floor plan segmentation, outperforming existing methods through novel attention mechanisms.

Contribution

It proposes a new Offset-Guided Attention mechanism and a Feature Fusion Attention module to improve semantic consistency in floor plan segmentation.

Findings

Improved room-level semantic consistency demonstrated.

Outperforms existing methods quantitatively.

Qualitative results show enhanced visual quality.

Abstract

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.