We propose a simple yet effective metric that measures structural similarity
between visual instances of architectural floor plans, without the need for
learning. Qualitatively, our experiments show that the retrieval results are
similar to deeply learned methods. Effectively comparing instances of floor
plan data is paramount to the success of machine understanding of floor plan
data, including the assessment of floor plan generative models and floor plan
recommendation systems. Comparing visual floor plan images goes beyond a sole
pixel-wise visual examination and is crucially about similarities and
differences in the shapes and relations between subdivisions that compose the
layout. Currently, deep metric learning approaches are used to learn a
pair-wise vector representation space that closely mimics the structural
similarity, in which the models are trained on similarity labels that are
obtained by Intersection-over-Union (IoU). To compensate for the lack of
structural awareness in IoU, graph-based approaches such as Graph Matching
Networks (GMNs) are used, which require pairwise inference for comparing data
instances, making GMNs less practical for retrieval applications. In this
paper, an effective evaluation metric for judging the structural similarity of
floor plans, coined SSIG (Structural Similarity by IoU and GED), is proposed
based on both image and graph distances. In addition, an efficient algorithm is
developed that uses SSIG to rank a large-scale floor plan database. Code will
be openly available.

提出了一种基于图像和图形距离的评估指标 SSIG（基于 IoU 和 GED 的结构相似性），用于判断平面图的结构相似性，并开发了一种有效的算法，通过 SSIG 对大规模的平面图数据库进行排序。

SSIG：用于楼层平面图相似度的视觉引导的图编辑距离

SSIG: A Visually-Guided Graph Edit Distance for Floor Plan Similarity

The recognition of information in floor plan data requires the use of
detection and segmentation models. However, relying on several single-task
models can result in ineffective utilization of relevant information when there
are multiple tasks present simultaneously. To address this challenge, we
introduce MuraNet, an attention-based multi-task model for segmentation and
detection tasks in floor plan data. In MuraNet, we adopt a unified encoder
called MURA as the backbone with two separated branches: an enhanced
segmentation decoder branch and a decoupled detection head branch based on
YOLOX, for segmentation and detection tasks respectively. The architecture of
MuraNet is designed to leverage the fact that walls, doors, and windows usually
constitute the primary structure of a floor plan's architecture. By jointly
training the model on both detection and segmentation tasks, we believe MuraNet
can effectively extract and utilize relevant features for both tasks. Our
experiments on the CubiCasa5k public dataset show that MuraNet improves
convergence speed during training compared to single-task models like U-Net and
YOLOv3. Moreover, we observe improvements in the average AP and IoU in
detection and segmentation tasks, respectively.Our ablation experiments
demonstrate that the attention-based unified backbone of MuraNet achieves
better feature extraction in floor plan recognition tasks, and the use of
decoupled multi-head branches for different tasks further improves model
performance. We believe that our proposed MuraNet model can address the
disadvantages of single-task models and improve the accuracy and efficiency of
floor plan data recognition.

MuraNet 是一种面向平面图数据中分割和检测任务的注意力多任务模型，通过统一编码器和分离的分割解码器分支以及基于 YOLOX 的独立检测头支路，提高了特征提取和相关任务的准确性和效率。