论文标题
偏移引导的注意网络，用于室内意识平面图细分
Offset-Guided Attention Network for Room-Level Aware Floor Plan Segmentation
论文作者
论文摘要
认识平面图是一项具有挑战性和流行的任务。尽管为此任务提出了许多最近的方法，但他们通常无法做出室内统一的预测。具体而言，可以在单个房间中分配多个语义类别，这严重限制了其视觉质量和适用性。在本文中，我们提出了一种新颖的方法，可以通过新提出的偏移引导的注意机制来识别平面图布局，以提高房间内的语义一致性。此外，我们提出了一个功能融合注意模块，该模块利用渠道的关注来鼓励房间，墙壁和门预测的一致性，从而进一步增强房间级别的语义一致性。实验结果表明了我们的方法能够提高房间级的语义一致性，并且在定性和定量上都优于现有作品。
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.
