We present PolyGNN, a polyhedron-based graph neural network for 3D building
reconstruction from point clouds. PolyGNN learns to assemble primitives
obtained by polyhedral decomposition via graph node classification, achieving a
watertight, compact, and weakly semantic reconstruction. To effectively
represent arbitrary-shaped polyhedra in the neural network, we propose three
different sampling strategies to select representative points as
polyhedron-wise queries, enabling efficient occupancy inference. Furthermore,
we incorporate the inter-polyhedron adjacency to enhance the classification of
the graph nodes. We also observe that existing city-building models are
abstractions of the underlying instances. To address this abstraction gap and
provide a fair evaluation of the proposed method, we develop our method on a
large-scale synthetic dataset covering 500k+ buildings with well-defined ground
truths of polyhedral class labels. We further conduct a transferability
analysis across cities and on real-world point clouds. Both qualitative and
quantitative results demonstrate the effectiveness of our method, particularly
its efficiency for large-scale reconstructions. The source code and data of our
work are available at this https URL

采用基于多面体的图神经网络模型 PolyGNN 对点云进行 3D 建筑重建，通过多面体分解网络学习组合原语，通过图节点分类实现紧密、紧凑和弱语义重构。

PolyGNN: 三维建筑点云重建的基于多面体的图神经网络

PolyGNN: Polyhedron-based Graph Neural Network for 3D Building  Reconstruction from Point Clouds

The goal of the Semantic Scene Completion (SSC) task is to simultaneously
predict a completed 3D voxel representation of volumetric occupancy and
semantic labels of objects in the scene from a single-view observation. Since
the computational cost generally increases explosively along with the growth of
voxel resolution, most current state-of-the-arts have to tailor their framework
into a low-resolution representation with the sacrifice of detail prediction.
Thus, voxel resolution becomes one of the crucial difficulties that lead to the
performance bottleneck.
In this paper, we propose to devise a new geometry-based strategy to embed
depth information with low-resolution voxel representation, which could still
be able to encode sufficient geometric information, e.g., room layout, object's
sizes and shapes, to infer the invisible areas of the scene with well
structure-preserving details. To this end, we first propose a novel 3D
sketch-aware feature embedding to explicitly encode geometric information
effectively and efficiently. With the 3D sketch in hand, we further devise a
simple yet effective semantic scene completion framework that incorporates a
light-weight 3D Sketch Hallucination module to guide the inference of occupancy
and the semantic labels via a semi-supervised structure prior learning
strategy. We demonstrate that our proposed geometric embedding works better
than the depth feature learning from habitual SSC frameworks. Our final model
surpasses state-of-the-arts consistently on three public benchmarks, which only
requires 3D volumes of 60 x 36 x 60 resolution for both input and output. The
code and the supplementary material will be available at
this https URL

本文提出了一种基于几何学的策略来将深度信息与低分辨率体素表示相结合，通过 3D 素描感知特征嵌入编码几何信息并采用半监督先验学习策略来引导占用推理和语义标签的推断，该方案在三个公共基准测试中一致优于现有技术。