Modified Swiss Dwellings: a Machine Learning-ready Dataset for Floor Plan Auto-Completion at Scale

<em>Modified Swiss Dwellings</em>The Modified Swiss Dwellings (MSD) dataset is a <strong>machine learning-ready dataset for floor plan auto-completion at scale</strong>. The MSD dataset is derived from the Swiss Dwellings database (v3.0.0). The MSD dataset (train split) contains <strong>4167 floor plans</strong> of <strong>single- as well as multi-unit building complexes across Switzerland</strong>, hence extending the building scale w.r.t. of other well know floor plan datasets like the RPLAN dataset. Since the MSD dataset will be part of a challenge @ ICCV in Paris, 2023, October 3, the <strong>test split is not yet made public</strong>. This will be added after the submission deadline of the challenge, which will be around mid September 2023.<br><em>Cleaning, filtering, and processing</em>All cleaning, filtering, and processing is done in Python. The Swiss Dwellings database is cleaned and filtered on residential building complexes that have a minimum room count (&gt;10) and have at least 2 "Zone 2" rooms (<em>e.g.</em>, living room, corridor, kitchen, dining). A graph extraction algorithm fully based on the `shapely` and `networkx` libraries in Python was developed to extract the access graphs from the filtered floor plans.<br><em>Dataset structure</em>The MSD dataset contains 3 files.<br>1) A README.md file explaining the dataset.2) A training set ZIP archive, containing 4 folders: `graph_in` [&lt;index&gt;.pickle], `struct_in` [&lt;index&gt;.npy], `full_out` [&lt;index&gt;.npy], and `graph_out` [&lt;index&gt;.pickle]. Naming is consistent across all folders, meaning that an instance from `graph_in` with name "&lt;index&gt;.pickle" is from the same floor plan as an instance from `full_out` with name "&lt;index&gt;.npy".3) A test set ZIP archive, containing 2 folders: `graph_in` and `struct_in` (similarly structured as the training data; but obviously with withheld annotations.)<br><em>Floor plan auto-completion</em>The MSD dataset is developed with the goal for the computer science community to develop (deep learning) models for the task of floor plan auto-completion. The floor plan auto-completion task takes as input the boundary of a building, the structural elements necessary for the building’s structural integrity, and a set of user constraints formalized in a graph structure, with the goal of automatically generating the full floor plan. Specifically, the goal is to learn the correlation between the the joint distribution of `graph_in` and `struct_in` with that of `full_out`. `graph_out` is provided when researchers want to use / develop methods from graph signal processing, or graph machine learning specifically.<br><em>GIthub guidelines</em>Github page.

<em>改良版瑞士住宅数据集（Modified Swiss Dwellings）</em>改良版瑞士住宅数据集（MSD）是<strong>可用于大规模平面图自动补全的机器学习就绪型数据集</strong>。该数据集源自瑞士住宅数据库（v3.0.0）。其训练集包含<strong>4167套平面图</strong>，覆盖瑞士境内的单户及多户建筑群，在建筑规模上优于RPLAN等知名平面图数据集。由于MSD数据集将作为2023年10月3日于巴黎举办的ICCV会议挑战赛的参赛数据集，<strong>测试集暂未公开</strong>，将于2023年9月中旬左右的挑战赛提交截止日期后发布。
<em>清洗、筛选与预处理流程</em>所有清洗、筛选及预处理工作均基于Python完成。首先对瑞士住宅数据库进行筛选，保留房间数量不少于10间，且至少包含2个“二区”房间（例如客厅、走廊、厨房、餐厅）的住宅建筑群。随后开发了一套完全基于Python的`shapely`与`networkx`库的图提取算法，从筛选后的平面图中提取访问图。
<em>数据集结构</em>MSD数据集包含3个文件：
1. README.md文件，用于对数据集进行说明；
2. 训练集压缩包，内含4个文件夹：`graph_in`（<index>.pickle）、`struct_in`（<index>.npy）、`full_out`（<index>.npy）以及`graph_out`（<index>.pickle）。所有文件夹的命名规则保持统一，即`graph_in`中名为<index>.pickle的样本与`full_out`中同名的<index>.npy样本来自同一套平面图；
3. 测试集压缩包，内含`graph_in`与`struct_in`两个文件夹（结构与训练集一致，但未公开标注信息）。
<em>平面图自动补全任务</em>MSD数据集的开发目标是助力计算机科学领域研究者开发用于平面图自动补全任务的（深度学习）模型。平面图自动补全任务的输入为建筑物边界、保障建筑结构完整性所需的结构元素，以及一组以图结构形式形式化的用户约束，其目标是自动生成完整的平面图。具体而言，该任务旨在学习`graph_in`与`struct_in`的联合分布与`full_out`分布之间的关联关系。若研究者希望使用或开发图信号处理或专门的图机器学习相关方法，则可借助`graph_out`文件开展相关工作。
<em>GitHub使用指南</em>GitHub页面。