3D building models with facade details are playing an important role in many
applications now. Classifying point clouds at facade-level is key to create
such digital replicas of the real world. However, few studies have focused on
such detailed classification with deep neural networks. We propose a method
fusing geometric features with deep learning networks for point cloud
classification at facade-level. Our experiments conclude that such early-fused
features improve deep learning methods' performance. This method can be applied
for compensating deep learning networks' ability in capturing local geometric
information and promoting the advancement of semantic segmentation.

融合几何特征和深度学习网络的立面级别点云分类方法提高了深度学习方法的性能，可以用来补偿深度学习网络在捕捉局部几何信息方面的能力，并推动语义分割的进展。

使用几何特征和深度学习网络对立面点云进行分类

Classifying point clouds at the facade-level using geometric features  and deep learning networks

In this paper we present our workflow to automatically reconstruct 3D
building models based on 2D building polygons and a LiDAR point cloud. The
workflow generates models at different levels of detail (LoDs) to support data
requirements of different applications from one consistent source. Specific
attention has been paid to make the workflow robust to quickly run a new
iteration in case of improvements in an algorithm or in case new input data
become available. The quality of the reconstructed data highly depends on the
quality of the input data and is monitored in several steps of the process. A
3D viewer has been developed to view and download the openly available 3D data
at different LoDs in different formats. The workflow has been applied to all 10
million buildings of The Netherlands. The 3D service will be updated after new
input data becomes available.

本文介绍了基于 2D 建筑多边形和 LiDAR 点云自动重建 3D 建筑模型的工作流程，该工作流程生成不同层次细节（LoDs）的模型，并监控数据质量以满足不同应用的数据需求。

自动化重建荷兰全部 1000 万栋房屋的 LoD2 和 LoD1 模型

Automated 3D reconstruction of LoD2 and LoD1 models for all 10 million  buildings of the Netherlands

While three-dimensional (3D) building models play an increasingly pivotal
role in many real-world applications, obtaining a compact representation of
buildings remains an open problem. In this paper, we present a novel framework
for reconstructing compact, watertight, polygonal building models from point
clouds. Our framework comprises three components: (a) a cell complex is
generated via adaptive space partitioning that provides a polyhedral embedding
as the candidate set; (b) an implicit field is learned by a deep neural network
that facilitates building occupancy estimation; (c) a Markov random field is
formulated to extract the outer surface of a building via combinatorial
optimization. We evaluate and compare our method with state-of-the-art methods
in generic reconstruction, model-based reconstruction, geometry simplification,
and primitive assembly. Experiments on both synthetic and real-world point
clouds have demonstrated that, with our neural-guided strategy, high-quality
building models can be obtained with significant advantages in fidelity,
compactness, and computational efficiency. Our method also shows robustness to
noise and insufficient measurements, and it can directly generalize from
synthetic scans to real-world measurements. The source code of this work is
freely available at this https URL

本文提出了一种通过自适应空间划分生成细胞复合体、利用深度神经网络了解区域内占用情况、通过组合优化提取建筑物外表面的新型框架来从点云中重建紧凑、完全、多边形建筑模型的方法，并通过实验表明该方法具有较高的保真度、紧凑性和计算效率。

使用深度隐式场从点云中重建紧凑建筑模型

Reconstructing Compact Building Models from Point Clouds Using Deep  Implicit Fields

We introduce BuildingNet: (a) a large-scale dataset of 3D building models
whose exteriors are consistently labeled, (b) a graph neural network that
labels building meshes by analyzing spatial and structural relations of their
geometric primitives. To create our dataset, we used crowdsourcing combined
with expert guidance, resulting in 513K annotated mesh primitives, grouped into
292K semantic part components across 2K building models. The dataset covers
several building categories, such as houses, churches, skyscrapers, town halls,
libraries, and castles. We include a benchmark for evaluating mesh and point
cloud labeling. Buildings have more challenging structural complexity compared
to objects in existing benchmarks (e.g., ShapeNet, PartNet), thus, we hope that
our dataset can nurture the development of algorithms that are able to cope
with such large-scale geometric data for both vision and graphics tasks e.g.,
3D semantic segmentation, part-based generative models, correspondences,
texturing, and analysis of point cloud data acquired from real-world buildings.
Finally, we show that our mesh-based graph neural network significantly
improves performance over several baselines for labeling 3D meshes.

BuildingNet 是一个包含多个建筑类别的大规模 3D 建模数据集，使用图神经网络对建筑模型进行网格标注，并展示了图神经网络在 3D 网格标注方面的显著性能优势。