Building Information Modeling (BIM) technology is a key component of modern
construction engineering and project management workflows. As-is BIM models
that represent the spatial reality of a project site can offer crucial
information to stakeholders for construction progress monitoring, error
checking, and building maintenance purposes. Geometric methods for
automatically converting raw scan data into BIM models (Scan-to-BIM) often fail
to make use of higher-level semantic information in the data. Whereas, semantic
segmentation methods only output labels at the point level without creating
object level models that is necessary for BIM. To address these issues, this
research proposes a hybrid semantic-geometric approach for clutter-resistant
floorplan generation from laser-scanned building point clouds. The input point
clouds are first pre-processed by normalizing the coordinate system and
removing outliers. Then, a semantic segmentation network based on PointNet++ is
used to label each point as ceiling, floor, wall, door, stair, and clutter. The
clutter points are removed whereas the wall, door, and stair points are used
for 2D floorplan generation. A region-growing segmentation algorithm paired
with geometric reasoning rules is applied to group the points together into
individual building elements. Finally, a 2-fold Random Sample Consensus
(RANSAC) algorithm is applied to parameterize the building elements into 2D
lines which are used to create the output floorplan. The proposed method is
evaluated using the metrics of precision, recall, Intersection-over-Union
(IOU), Betti error, and warping error.

本研究提出了一种混合的语义几何方法，通过基于 PointNet ++ 的语义分割网络与地理推理规则相结合，将激光扫描建筑点云转化为二维平面图，同时对精度、召回率、交并比、Betti 误差和形变误差等方面进行评估。

一种混合语义几何方法应对建筑点云中的混杂干扰生成楼层平面图

A Hybrid Semantic-Geometric Approach for Clutter-Resistant Floorplan  Generation from Building Point Clouds

This paper proposes a novel generative adversarial layout refinement network
for automated floorplan generation. Our architecture is an integration of a
graph-constrained relational GAN and a conditional GAN, where a previously
generated layout becomes the next input constraint, enabling iterative
refinement. A surprising discovery of our research is that a simple
non-iterative training process, dubbed component-wise GT-conditioning, is
effective in learning such a generator. The iterative generator also creates a
new opportunity in further improving a metric of choice via meta-optimization
techniques by controlling when to pass which input constraints during iterative
layout refinement. Our qualitative and quantitative evaluation based on the
three standard metrics demonstrate that the proposed system makes significant
improvements over the current state-of-the-art, even competitive against the
ground-truth floorplans, designed by professional architects.

通过一种基于生成对抗网络的自动化平面图生成方法，使用基于图的相关生成对抗网络和条件生成对抗网络的集成，并在迭代布局细化过程中使用之前生成的布局作为下一个输入约束。该研究提出了一种名为组件方式 GT 调节的简单非迭代训练过程，有效地学习这样的生成器，通过元优化技术控制迭代布局优化过程中传递哪些输入约束的时间，从而进一步提高所选指标。基于三种标准指标的定性和定量评估显示：所提出的系统在当前最先进技术水平上取得了重大进展，甚至可以与由专业建筑师设计的地面平面图媲美。